DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCEICRP ref: 4829-8874-8978

1
2
3

4

Annals of the ICRP

5
6
7
8

ICRP PUBLICATION 1XX

9
10
11

Radiation Detriment Calculation Methodology

12
13
14
15
16

Editor-in-Chief

17

C.H. CLEMENT

18
19

Associate Editor

20

H. FUJITA

21
22
23
24

Authors on behalf of ICRP
E. Cléro, L. Vaillant, W. Zhang, N. Hamada, D. Preston, D. Laurier, N. Ban

25
26
27
28

PUBLISHED FOR

29

The International Commission on Radiological Protection

30
31
32
33
34
35
36
37
38
39

by

40

[SAGE logo]
Please cite this issue as ‘ICRP, 20xx. Radiation detriment calculation methodology.
ICRP Publication 1XX. Ann. ICRP xx(x).’DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
41
42

CONTENTS

43[Guest] Editorial ................................................................................................................................ 1

44

Abstract ............................................................................................................................................. 245

MAIN POINTS ................................................................................................................................. 346

EXECUTIVE SUMMARY................................................................................................................ 4

47

1.

INTRODUCTION..................................................................................................................... 6

48
49
50
51
52

2.

HISTORY OF RADIATION DETRIMENT CALCULATION .................................................. 8
2.1.Publication 26 ......................................................................................................................... 8
2.2.Publications 27 and 45 ............................................................................................................ 9
2.3.Publication 60 ....................................................................................................................... 10
2.4.Publication 103 ..................................................................................................................... 13

53
54
55
56

3.

CALCULATION OF RADIATION DETRIMENT ................................................................. 14
3.1.Nominal risk calculation ........................................................................................................ 15
3.2.Severity adjustment ............................................................................................................... 33
3.3.Relation between radiation detriment and effective dose: tissue weighting factors wT ............. 36

57
58
59
60

4.

SENSITIVITY OF RADIATION DETRIMENT CALCULATION ......................................... 38
4.1.Parameters involved in the calculation of the nominal risk ..................................................... 38
4.2.Parameters related to adjustment for severity ......................................................................... 45
4.3.Summary of sensitivity analysis ............................................................................................. 48

61
62
63
64
65
66

5.

POTENTIAL EVOLUTION ................................................................................................... 50
5.1.Input information................................................................................................................... 50
5.2.Variation with sex and age ..................................................................................................... 52
5.3.Exposure scenario .................................................................................................................. 53
5.4.Consideration of non-cancer effects ....................................................................................... 53
5.5.Transparency and comprehensibility ...................................................................................... 54

67
68
69
70

6.

SUMMARY AND CONCLUSIONS ....................................................................................... 56
6.1.Calculation of radiation detriment .......................................................................................... 56
6.2.Sensitivity of radiation detriment ........................................................................................... 56
6.3.Suggestions for future improvements ..................................................................................... 57

71

REFERENCES ................................................................................................................................ 59

72

ABBREVIATION LIST .................................................................................................................. 61

73

GLOSSARY.................................................................................................................................... 62

74

ACKNOWLEDGEMENTS ............................................................................................................. 67

75
76
77
78

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE[GUEST] EDITORIAL

79
80
81
82
83

To be drafted84

1

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116

RADIATION DETRIMENT CALCULATION METHODOLOGYICRP PUBLICATION 14X
Approved by the Commission in XX, 20XX
Abstract-Radiation detriment is a concept used to quantify the harmful stochastic effects of
low-level radiation exposure to the human population. It is determined from lifetime risk ofcancer for a set of tissues and organs taking into account their severity in terms of lethality,
quality of life, and years of life lost. It also considers heritable effects. The radiation detrimentis estimated as a sex- and age-averaged risk indicator for a composite reference population.
This report provides a historical review of the detriment calculation methodology adopted by
the International Commission on Radiological Protection (ICRP) since Publication 26 and adetailed description of the whole computation process used in Publication 103. It clarifies data
sources, risk models, computational methods and rationale for the choice of parameter values.
The parameters that have the greatest influence on the radiation detriment calculation are also
identified based on a series of sensitivity analyses. They include dose and dose-rate
effectiveness factor (DDREF), age at exposure, sex difference and lethality fraction. Although
the current scheme of radiation detriment calculation is well established, it may need to evolve
to take into account changes in baseline reference data (mortality, cancer incidence andlethality) in recent decades and progress in scientific understanding of radiation health effects.
In this perspective, the report suggests ways to update and improve the estimation of key
parameters for the calculation of radiation detriment, such as the reference population data andcancer severity. There is also room for improvement in cancer risk models based on theaccumulation of recent epidemiological findings. Finally, the importance of improving thecomprehensibility of the radiation detriment concept and the transparency of its calculation
methodology is emphasised.
© 20YY ICRP. Published by SAGE.
Keywords: Radiation detriment; Nominal risk; Sensitivity analysis; Stochastic effects

1172

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE

MAIN POINTS

118
119
120
121
122



Radiation detriment is a concept used to quantify the health impact of stochastic
effects (cancer and heritable effects) from low-dose and low-dose-rate radiation
exposures, considering both the probability of occurrence and the severity of theseeffects.

123
124
125
126



The method for calculating radiation detriment consists of two main parts:
calculation of nominal risk (average estimate of the lifetime cancer risk and the riskof heritable effects associated with radiation exposure) and adjustment for lethality,
quality of life and years of life lost.

127
128
129



Sensitivity analysis identified dose and dose-rate effectiveness factor (DDREF), age
at exposure, sex and lethality fraction as parameters having a large impact on theestimation of radiation detriment.

130
131
132
133



Radiation detriment needs to be updated considering changes in reference population
data, variation of cancer risk with sex and age and between different populations,cancer severity parameters, improvement in cancer risk models, and review of risk
estimates for heritable effects.

1343

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
135

EXECUTIVE SUMMARY

136
137
138
139
140
141

(a) The concept of radiation detriment has been developed by the Commission for the
purpose of radiological protection. It is defined as the excess of stochastic health effects in agroup of exposed individuals to low-level radiation and their descendants compared to a nonexposed group. It is determined from sex-averaged and age-at-exposure-averaged lifetime risk
estimates for a set of organs and tissues, taking into account the severity in terms of lethality,
quality of life, and years of life lost.

142
143
144

(b) Radiation detriment at low doses or low dose-rates is quantified assuming a linearnon-threshold (LNT) dose-response relationship for stochastic effects and applying a dose anddose-rate effectiveness factor (DDREF) of 2 for solid cancer.

145
146
147
148
149
150
151
152
153
154

(c) The methodology for calculating radiation detriment has developed over decades
since the concept was first introduced in Publication 22. The most recent method in Publication
103 consists of two main parts. The first part is the calculation of nominal risks, which areaverage estimates over age groups of the lifetime cancer incidence risks and the risk of heritableeffects associated with radiation exposure. The lifetime risk of cancer incidence is calculated
separately for four reference populations (males and females of Euro-American and Asian
populations) except for bone and skin cancers, and results are averaged across sexes andregions. The second part is the adjustment for the severity of the consequences. All calculation
steps are executed separately for individual organs/tissues or group of tissues, and the resulting
values are added up to give the total radiation detriment.

155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177

(d) The calculation of the nominal cancer risk involves a number of sequential steps. The
procedure adopted in Publication 103 is summarised below:• Baseline cancer rates are computed using cancer incidence data from selected Asianand Euro-American populations to compile rates for representative populations indifferent parts of the world.
• Cancer risk models are developed from the analysis of cancer incidence data from theLife Span Study (LSS) of the atomic bomb survivors. The excess relative risk (ERR)
and the excess absolute risk (EAR) are modelled with modifying effects of sex, age at
exposure, and attained age.
• The minimum latency period is assumed to be five years for solid cancers and two years
for leukaemia.
• The risk of exposure-induced cancer incidence (REIC) is calculated for an acuteexposure of 0.1 Gy and multiplied by 10 to obtain the lifetime risk at 1 Gy for eachcancer site. It is computed for each age at exposure, 0 to 84 years for the whole
population and 18 to 64 years for adult workers, by cumulating the risk until the attained
age reaches 90 years.
• The weighted mean of REIC for each age at exposure is calculated to give the ageaveraged lifetime risk, the weight being proportional to the age distribution of thereference population.
• The ERR and EAR lifetime risks are weight-averaged according to weighting factors
specified for each organ or tissue.
• The lifetime risk estimates are adjusted downward by a DDREF of 2 for all cancer sites
except for leukaemia for which a linear-quadratic model is used.

4

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
178
179
180
181

• The unweighted mean of the resulting values between the four reference populations
yields a nominal risk for each cancer site.
• The total nominal risk is calculated as the sum of nominal risks estimated for 13
categories of cancer with the consideration of additional risk reflecting heritable effects.

182
183
184
185
186

(e) The calculation of radiation detriment is based on a weighting procedure in which
nominal cancer risks are adjusted by three parameters reflecting lethality, quality of life andyears of life lost. These three parameters are independent of radiation dose. Their determination
is partly based on expert judgement, and the values used do not consider differences with age,sex, or between populations.

187
188
189
190
191
192
193
194

(f)
Sensitivity analysis on radiation detriment was conducted for nine solid cancers and agroup of other solid cancers to examine the potential impact of assumptions made in thecalculations. Depending on their level of impact, three categories were identified.
• Limited impact: minimum latency period, maximum attained age, lifetime risk
calculation method, minimum quality-of-life factor, and relative years of cancer-freelife lost.
• Noticeable impact on some cancer sites: reference population and transfer model.
• Large impact: DDREF, age at exposure, sex, and lethality fraction.

195
196
197
198
199
200
201
202

(g) Considering the variation of cancer risk with sex and age, it is advisable to calculate
lifetime risks separately for sexes and selected ages (age groups) and average them in the laststage to obtain a nominal value. This approach distinguishes science-based risk assessment
from the subsequent integration of information for protection purposes, thus providing a better
understanding of the construction of the radiation detriment. Sex- and age-related variation
should also be considered in determining the values of tissue weighting factors, wT based on
the relative detriment. Description of the impact of sex and age at exposure on the relative
detriment helps to understand the distribution range and the representativeness of wT.

203
204
205
206
207
208

(h) Radiation detriment needs to evolve depending on the advances in healthcare andscientific understanding of radiation effects. It will be necessary to update reference population
data and cancer severity parameters in the near future. Cancer risk models should be improved
and the weighting scheme for transferring risks needs to be validated based on up-to-date
epidemiological data. It is also desirable to review the risk estimate for heritable effects taking
into account recent studies.

209
210
211
212

(i)
There is considerable uncertainty about the existence or not of a threshold forcirculatory disease and cataract and the shape of the dose-response curve at low doses if there
is no threshold. Whether or not to include them in the calculation of the radiation detrimentcurrently remains an open question.

213
214
215
216
217
218

(j)
Ensuring transparency and traceability of the radiation detriment calculation isimportant. A full description of the calculation steps of the radiation detriment is necessary,and consideration should be given to the development of an open-source software to performthese calculations. It is also desirable to improve the way of expressing radiation detriment andto illustrate the data of reference populations so that non-specialists can have a balanced
perspective on the health risks of radiation.

5

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
219

1. INTRODUCTION

220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266

(1) The health effects of radiation are classified into two categories, deterministic effects(harmful tissue reactions) and stochastic effects, i.e., cancer and heritable effects. For low-dose,
low-dose-rate exposures, stochastic effects are assumed for radiological protection purposes tofollow a dose response with no threshold.
(2) Radiation-associated cancers generally have long latencies, and the length of life lostdepends on the distribution of age of onset of the cancers. There are also considerable
differences in fatality among cancer sites. To appropriately assess the risk of cancer attributed
to radiation exposure, the severity as well as its probability needs to be taken into account. The
same holds true for heritable effects as they include a wide range of abnormalities.
(3) The Commission initially introduced the concept of detriment as the mathematical‘expectation’ of the harm incurred in a group from a radiation dose (ICRP, 1973, 1977a). It
was later replaced by a multi-dimensional concept to properly represent the different aspectsof the health impact in order to: (i) assess the consequences of continued or cumulative
exposures to recommend dose limits, (ii) compare the consequences of different distributionsof equivalent dose within the body and thence to select a set of tissue weighting factors, and(iii) provide a basis for assessing the valuation of a unit of effective dose for use, for example,
in the optimisation of protection within a practice (ICRP, 1991).
(4) The Commission has developed a methodology for aggregating different facets of the
detriment into a single quantity. It is called radiation detriment, which is calculated as an
adjusted excess risk from radiation exposure using this methodology. It is determined fromlifetime risk of cancer and heritable effects as an average over different populations, sexes andages at exposure, taking into account the severity of the disease in terms of lethality, quality oflife, and years of life lost. Calculated values for individual organs/tissues or group of tissues
are added up to give the total radiation detriment.
(5) Radiation detriment at low doses or low dose-rates is quantified assuming a linear-nonthreshold (LNT) dose-response relationship except for leukaemia, which is based on a linearquadratic dose response. A dose and dose-rate effectiveness factor (DDREF) is applied to solidcancer to adjust the risk estimated from the epidemiological data of high-dose and high-doserate exposures. High-dose exposures for which tissue reactions are of concern are strictly outof scope of this methodology, although it does not mean that stochastic effects do not occur at
higher dose levels. It is also not recommended to use radiation detriment for assessing thehealth risk of acute exposures at intermediate dose ranges (e.g. a few hundred millisieverts).
At these levels of dose, it would be inappropriate to rely on the LNT model adjusted by the
DDREF.
(6) The system of radiological protection applies to any individual who is exposed toionising radiation, and methods of controlling sources of exposure are usually applied without
reference to individual profiles of those exposed. In this regard, it is desirable to set standards
and to optimise protection in ways that are independent of age, sex and region of the world.
This approach emphasises respect for equity and fairness from an ethical point of view.
Radiation detriment is therefore computed by averaging the risk estimates over age groups,
both sexes and geographical regions to represent the risk for a nominal population. As thecalculation process involves the risk transfer and averaging across populations with differingbaseline cancer rates, the nominal population is regarded as a mixture of people with different
factors governing individual responses to radiation including not only non-modifiable factors,
but also modifiable lifestyle factors. This was clearly demonstrated in ICRP Publication 115(ICRP, 2010), in which the nominal risk coefficient for radon exposure was defined for a mixed
adult population of non-smokers and smokers.
6

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285

(7) The Commission believes that the system of radiological protection, which has beendeveloped on the basis of the nominal risk approach, is simple, non-discriminatory and globally
applicable while achieving adequate protection for every individual regardless of age, sex andregion of the world. Radiation detriment can be used for prospective risk assessment of
exposure situations for radiological protection purposes or to assess risks in retrospective
situations for exposures of identified individuals. However, it should be noticed that there are
significant differences in risk between sexes and in respect of age at exposure. For theestimation of the likely consequences of an exposure of a given individual or population, it ispreferable to use specific data relating to the exposed individuals when they are available.
(8) Radiation detriment is intended to be a reliable, robust indicator of the overall burden ofstochastic effects, and as such, it needs to reflect the latest scientific information and thechanges in population health statistics. The methodology of its calculation has been developed
over decades to meet these requirements. This report provides a historical review of themethodology for calculating radiation detriment adopted by ICRP since Publication 26 (ICRP,
1977) and a detailed description of the computation process used in Publication 103 (ICRP,
2007). Data sources, risk models, computational methods and the rationale for the parametervalues adopted are detailed for each step of the process. This is followed by a series ofsensitivity analyses to identify the primary sources of uncertainty in the radiation detrimentcalculation. Based on the results, some key issues are discussed for future consideration.

286

7

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
287

2. HISTORY OF RADIATION DETRIMENT CALCULATION

288

2.1. Publication 26

289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330

(9) The concept of detriment was first introduced in ICRP Publication 22 (ICRP, 1973). It
was maintained in Publication 26 (ICRP, 1977a) and defined as follows: ‘The deleterious
effects of exposure to radiation may be of many kinds. Among the effects on health there may
be both stochastic and non-stochastic effects in the exposed individual and stochastic effects inlater generations. … The Commission has introduced the concept of detriment to identify, andwhere possible to quantify, all these deleterious effects. In general, the detriment in a
population is defined as the mathematical “expectation” of the harm incurred from an
exposure to radiation, taking into account not only the probability of each type of deleterious
effect, but also the severity of the effect’.
(10)In Publication 26 (ICRP, 1977a), a quantitative value for the detriment at low dose andlow dose rate relied on a linear model. Publication 26 noted that linear extrapolations may leadto an overestimate of the radiation risks at low doses and low dose rates but endorsed this as a
cautious assumption. Additionally, while recognising that risks for some cancer sites were age
or sex dependent, the Commission judged that for radiological protection purposes sufficientaccuracy could be obtained by using an average value for each organ or tissue regardless ofage or sex for both workers and the general public. Detriment, specifically called ‘risk factor’in Publication 26, was expressed as the likelihood of fatal cancers and serious hereditaryabnormalities. It was quantified for the following organs/tissues: gonads (including both cancer
mortality and hereditary effects in the progeny), red bone marrow, bone, lung, thyroid, breastand ‘other tissues’.
(11)The risk factor for leukaemia was taken to be 20 10-4 Sv-1. A review by the Commission
concluded that bone was much less sensitive than breast, red bone marrow, lung and thyroid
and the risk factor for bone cancer was taken to be 5 10-4 Sv-1. The risk of lung cancer was
about the same as that for the development of leukaemia (e.g. 20 10-4 Sv-1). The sensitivity ofthe thyroid to the induction of cancer by radiation appeared to be higher than that of the redbone marrow for the development of leukaemia. However, the mortality from these thyroid
cancers being much lower than for leukaemia, the overall mortality risk factor was considered
to be 5 10-4 Sv-1. Based on data on the development of female breast cancer following radiation
exposure, it was suggested that, during reproductive life, the female breast might be one of themost radiosensitive tissues of the human body. There were indications that, under thesecircumstances, the risk factor for breast cancer could be a few times higher than that forleukaemia and the risk factor was taken to be 25 10-4 Sv-1. In addition to the tissues discussed
above, there were other tissues (e.g. stomach, lower large intestine, salivary glands and liver)for which there was evidence that radiation was also carcinogenic at moderate doses, but norisk factors were specified for them. It was estimated that the combined risk of malignancy inall remaining unspecified tissues was unlikely to exceed 50 10-4 Sv-1. For gonads, the risk factor
for hereditary effects over the first two generations was taken as about 40 10-4 Sv-1.
(12)Based on the values described above, the Commission concluded that the mortality risk
factor for radiation-induced cancers was about 125 10-4 Sv-1, as an average for both sexes andall ages, and that the average risk factor for hereditary effects could be taken as about 40 10-4
Sv-1. Results are summarised in Table 2.1.

8

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
331

Table 2.1. ICRP Publication 26 values for nominal mortality risk coefficients.
Risk factors (10-4 Sv-1)

Organ/tissue
CancerBone marrow
Bone surface

20
5

Breast

25

Lung

20

Thyroid5*

Remainder

50(Total cancer)
Hereditary effects(125)

GonadsNo specific organs listed.

40

332

*

333

2.2. Publications 27 and 45

334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357

(13)Publication 27 (ICRP, 1977b) provided supporting guidance to the generalrecommendations in Publication 26 (ICRP, 1977a) general recommendations. It aimed to
discuss ‘the problems entailed in comparing the safety of different industries including those
involving radiation exposure, taking account of the fact that the types of injury or induced
diseases, and their severity and relative frequencies, might differ completely in different
occupations’. By comparing different occupational risk, it aimed to support the value adopted
for the occupational dose limit in Publication 26.
(14)In order to compare different occupational risks, Publication 27 relied on the calculationof years of life lost for various risks. It concluded that ‘If fatal malignancies were induced at a
rate of 10–4 rem–1, with an equivalent life loss of 15 years for each including the periods ofillness from fatal and non-fatal malignancies, the life loss from somatic effects would amountto 1.5 man-years per 1000 man-years per rem of average occupational exposures’. The
calculation of the index of harm for ionising radiation took into account fatal cancer as well as
non-fatal cancer and associated years of life lost.
(15)The assessment of the index of harm in Publication 27 was revised in Publication 45(ICRP, 1985), based on more comprehensive data. For cancers induced by occupationalradiation exposure, the risk factors in Publication 26 were used as the frequency of fatal cases
per unit dose, and each case was assumed to bring a mean loss of 15 years of life expectancy
plus 1 additional year to take into account the period of illness prior to death (i.e. 16 years oflife lost per case). Lethality data of different types of cancer were reviewed to estimate theinduction rates and severity of the non-fatal (curable) component, which led to the weightingof 0.29/1.26 for them as shown in Table 2.2. The resultant life-loss detriment from all cancer
induction was 0.3 y Sv–1 in females and 0.2 y Sv–1 in males.

9DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE

Table 2.2. Weighting of detriment from curable cancers in Publication 45 (ICRP, 1985).

358

Organ/tissue

Risk of induction(10-2 Sv-1)

Severityof cure*Cured(10-2 Sv-1)

Fatal

CurableBreast

0.25

0.15

×

0.6

=

0.09

Bone marrow

0.20

0.01

×

0.95

=

0.01

Lung

0.20

0.01

×

0.95

=

0.01

Thyroid

0.05

1.0

×

0.05

=

0.05

Bone

0.05

0.01

×

0.85

=

0.01

Skin

0.01

1.0

×

0.01

=

0.01

Remainder

0.50

0.15

×

0.75

=

0.11

Total

1.26

2.33

0.29

359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376

*

377

2.3. Publication 60

378
379
380
381
382
383
384

(18)In its Publication 60 (ICRP, 1991), the Commission outlined that new information on
the risk of radiation-induced cancer in human populations had emerged since 1977 as well as
new experimental data in laboratory animals and cultured cells, leading to a reassessment of
Publication 26 (ICRP, 1977a) estimates of the probability of the carcinogenic effects ofradiation. The results for the relative probabilities of fatal cancer for males and females were
calculated for China, Japan, Puerto Rico, the U.K. and the U.S. for age 0–89 years and averaged.
This yielded the values given in the first column in Table 2.3. These values were used as the

The ratio of ‘fatal’ to ‘fatal plus curable’ cancers of the same type.

(16)For hereditary effects, the United Nations Scientific Committee on the Effects of Atomic
Radiation (UNSCEAR) 1982 report estimated years of life impaired or lost to be 0.63 years
per person.Gy1 of genetically significant radiation at equilibrium after continuous exposure(UNSCEAR, 1982). The genetically significant fraction of collective dose in the working
population was estimated from mean ages at conception, 30.6 years for fathers and 25.9 years
for mothers. Based on these parameters, life-loss detriment from occupational exposure at aconstant rate was assessed to be about one third in women and three quarters in men of thatfrom cancer. At a dose rate of 2 mSv year–1 as a representative exposure scenario for the
majority of workers, an index of harm expressed as years lost per 1000 worker-years was thus
0.6 and 0.2 for carcinogenic and hereditary effects in females, and 0.4 and 0.3 in males,
respectively.
(17)The effects of exposures during pregnancy were also taken into account on the basis that
intra-uterine death, mental retardation, cancer and hereditary effects were induced without
threshold. With an assumed frequency of 6.5 pregnancies per 100 worker-years of the female
population in employment, the index of harm was calculated to be 1.0 per 1000 female workeryears for exposure at 2 mSv year–1.

1

Units of dose are shown as in the original reference. Otherwise, Gy is used for nominal risks and Sv
for radiation detriments.
10

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
385
386
387
388

basis of the relative probabilities of cancer in organs for a nominal world population of all ages
from which to derive the detriment.
(19)In Publication 60 (ICRP, 1991), the following specific assumptions were made for the
thyroid, bone surface, skin and liver.

389
390
391
392
393
394

•

For thyroid, the UNSCEAR 1988 Report (UNSCEAR, 1988) and the U.S. National
Academy of Sciences’ Biological Effectiveness of Ionizing Radiation (BEIR) Report V(NRC, 1990) agreed that the best available estimates of risk to the thyroid were thosepresented in NCRP Report No. 80 (NCRP, 1985). These estimates gave a lifetime risk forfatal thyroid cancer of 7.5 10-4 Gy-1. The fatality rate was stated to be 0.1, thus theincidence was 75 10-4 Gy-1.

395
396
397
398

•

For bone surface, based on high linear energy transfer (LET) radiation data, the BEIR IV
report (NRC, 1990) provided an estimate of a lifetime incidence of about 133 10-4 Gy-1.
With a lethality fraction of 0.70, this became 93 10-4 Gy-1 and about 4.7 10-4 Sv-1 after
application of a quality factor (Q) of 20.

399
400
401
402

•

For skin, Publication 59 (ICRP, 1992) found the incidence of skin cancer to be 1000 10-4
Sv-1, while the fatality (or lethality) fraction was conservatively estimated as 0.2%. The
fatal skin cancer risk was presumed to be applicable at low doses and was thus taken to
be 2 10-4 Sv-1.

403
404
405
406

•

For liver, the data from thorotrast studies in West Germany, Portugal, Japan and Denmark
yielded about 300 10-4 fatal liver cancers per Gy. With a Q of 20, a nominal risk estimateof 15 10-4 Sv-1 was derived and applied also for low LET radiation.

11

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE

Table 2.3. Calculation of detriment in Publication 60* (ICRP, 1991).

407

Organ/tissue

Probability offatal cancer F(10-4 Sv-1)

Detriment**(10-4 Sv-1)

Relative
contribution
to the total
detriment

30

0.65

1.50

29.4

0.040

Bone marrow

50

2.06

1.01

104.0

0.143

Bone surface

5

1.00

1.30

6.5

0.009

Breast

20

1.21

1.50

36.4

0.050

Colon

85

0.83

1.45

102.7

0.141

Liver

15

1.00

1.05

15.8

0.022

Lung

85

0.90

1.05

80.3

0.111

Oesophagus

30

0.77

1.05

24.2

0.034

Ovary

10

1.12

1.30

14.6

0.020

Skin

2

1.00

2.00

4.0

0.006

Stomach

110

0.83

1.10

100.0

0.139

Thyroid

8

1.00

1.90

15.2

0.021

Remainder

50

0.91

1.29

58.9

0.081

133.3

0.183

725.3

1.000

Total

414
415
416

Relative
Relative
non-fatal
length ofcontribution
life lost l/l̄(2–k)

Bladder

Gonads
408
409
410
411
412
413

Severegenetic effects(10-4 Sv-1)

100

1.33

500

*

Definition of symbols
F : Probability of fatal cancerl : Expected years of life lostl̄ : Average of l for all cancers (15.0 years)
k : Lethality fraction**
Detriment is given by F (l/l̄ ) (2–k)(20)In addition to nominal estimates of fatal cancer, the detriment calculated in Publication
60 included three additional components:

417
418

•

A specific allowance for differences in lethality which resulted in different values of
expected life lost for fatal cancer originating in different organs;419•

An allowance for the morbidity resulting from induced non-fatal cancers; and

420
421

•

An allowance for the risk of serious hereditary disease in all future generations descended
from the irradiated individual.

422
423
424
425
426
427
428
429

(21)To allow for the detriment associated with non-fatal cancers, the detriment of eachcancer type included a non-fatal component weighted according to the lethality fraction k. Thus,
if in a given tissue there were F fatal cancers, the total number of cancers was F / k. The numberof non-fatal cancers was then (1 – k) F / k and the total weighted detriment was F + k [(1 – k)
F / k] or F (2 – k).
(22)Steps in the calculation of the detriment are detailed in Table 2.3. It shows how the
probability of fatal cancer of 500 (considering only fatal cancer) develops into a detriment of
725 per 10,000 person.Sv. This part of the methodology is based on risk characteristics
12

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
430
431
432
433

associated with cancer types and hereditary disease. It is not directly related to radiation
exposure.
(23)The relative contributions of the organs to the total detriment (last column) formed thebasis of the Commission’s tissue weighting factors.

434

2.4. Publication 103

435
436
437
438
439
440
441
442
443
444
445

(24)Publication 103 (ICRP, 2007) adopted a new calculation methodology. While themethods used were broadly similar to those used in Publication 60 (ICRP, 1991), modifications
were made in several aspects of the computations. Of these, one major change was the moveto base nominal risk calculations on cancer incidence data rather than on cancer mortality data.
For clarification, the detriment calculated using this methodology is specifically called‘radiation detriment’, and the term ‘detriment’ means radiation detriment hereafter unlessotherwise noted.
(25)The Publication 103 methodology of radiation detriment calculation is detailed in
Section 3 with an effort to avoid imprecisions and ambiguities (its outline is also provided in
Cléro et al., 2019). The description presented herein should be considered an improved andcorrected version of that provided in Publication 103.

446

13

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
447

3. CALCULATION OF RADIATION DETRIMENT

448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470

(26)The procedure for calculating radiation detriment is described in Annex A.4 of
Publication 103 (ICRP, 2007). Background information about cancer risk estimation is also
given in Annex B of Publication 60 (ICRP, 1991).
(27)This calculation procedure has two major parts each of which consists of sequential steps(Fig. 3.1). The first part is the calculation of the nominal risk: an estimate of the lifetime risk
associated with radiation exposure, including the risk of cancer and heritable diseases. Risk
estimates of cancer are averaged across sexes, ages at exposure and geographical regions foreach cancer site. The second part is the adjustment for severity, which takes into accountlethality, quality of life, and years of life lost. As shown in Fig. 3.1, only the first part depends
on radiation dose. The second part is virtually independent of radiation exposure, but reflects
the severity of cancer (also heritable disease for the gonads) of respective organs or tissues.
(28)In this publication, Gy is used as the dose unit for the calculation of nominal risk (Firstpart) and Sv is used for the calculation of radiation detriment (Second part).
(29)Averaging across sexes, ages or geographical regions is applied at different steps in theprocess of detriment calculation. The lifetime risk of cancer is calculated separately for males
and females, and for the two reference populations (except for bone and skin cancers), and theresults are averaged to estimate the nominal risk. The estimate of the excess risk of heritableeffects and the adjustment factors including the DDREF, the lethality fraction and theparameters related to quality of life are applied without distinguishing between sexes orpopulation groups. All steps are conducted in parallel for each organ and tissue separately, andthe resulting values are finally summed to give the total radiation detriment.

471
472
473
474

Fig. 3.1. Calculation procedure of radiation detriment in Publication 103 (ICRP, 2007).

14

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
475

3.1. Nominal risk calculation476

3.1.1. Cumulative baseline risk

477

3.1.1.1. Reference populations

478
479
480
481
482
483
484
485
486

(30)Composite baseline incidence rates of cancer were computed using cancer incidence
data from selected Asian and Euro-American populations with long-running cancer registries:
Shanghai (China), Osaka, Hiroshima and Nagasaki (Japan), Sweden, United Kingdom, and theSurveillance, Epidemiology, and End Results (SEER) program of the U.S. National Cancer
Institute. An unweighted average of the Asian and the Euro-American data was calculated toform a composite population. The aim was to compile rates for representative populations indifferent parts of the world. Population size data were obtained from the World HealthOrganization (WHO) international mortality statistics database (WHO population data filedownloaded April 22, 2003: http://www.who.int/healthinfo/mortality_data/en/).

487

3.1.1.2. Baseline cancer rates

488
489
490
491
492
493
494
495
496

(31)Population-based cancer incidence rates were obtained from the 8th edition of Cancer
Incidence in Five Continents (cancer rates measured by registries during the period 1993–1997(Parkin et al., 2002). Incidence data are available for all cancer sites except for bone and skin.
Average incidence rates were compiled for the Asian and Euro-American populations,separately for males and females and by 5-year age categories (from 0–4 to 90+), for theoesophagus, stomach, colon, liver, lung, female breast, ovary, bladder, thyroid, leukaemia,
leukaemia excluding chronic lymphocytic leukaemia (CLL), all solid cancers and all cancers
combined. In addition, mortality rates for each cancer category and for all causes combined
were also provided (Tables A.4.10 to A.4.17, in Publication 103 (ICRP, 2007)).

497

3.1.1.3. Survival functions

498
499
500
501

(32)The survival functions (Fig. 3.2) were derived from the mortality rates estimated for thefour reference populations (males and females each in Asian and Euro-American populations),
obtained from the 8th edition of Cancer Incidence in Five Continents (Parkin et al., 2002).

15

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
100%
90%

Survival function (%)

80%
70%
60%
50%
40%

Euro-American male

30%

Asian male20%

Euro-American female

10%

Asian female

0%
0

5

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90
Attained age (years)

502
503

Fig. 3.2. Survival function of reference populations.

504
505

3.1.1.4. Calculation of cumulative baseline cancer risk

506
507

(33)The lifetime baseline risk (LBR) is the cancer risk in the absence of radiation exposure
cumulated up to reaching the age of 90 years old.
% &%2!"#(%&'( , *) = -

% &'(

508
509
510
511
512
513
514
515
516
517
518
519

.' (%, *)/(%|%&'( , *)1%

where s = sex, amin = age at the beginning of risk, amax = maximum age (i.e. 90 years), µi (a,s)= age- and sex-specific baseline cancer incidence rates, and S(a|amin ,s) = survival function (i.e.
the sex-specific probability to be alive at age a for a person alive at age amin).
(34)For illustration, cumulative baseline risks are presented in Fig. 3.3 for all solid cancers,
Fig. 3.4 for non-CLL leukaemia, and Fig. 3.5 for female breast cancer incidence. For mostcancer sites, cumulative baseline risks are higher in males than in females (oesophagus, colon,lung, bladder, non-CLL leukaemia, and all solid cancers). In both sexes, stomach and livercancer incidence is higher for Asian than for Euro-American populations. For female breast
cancer, baseline rates vary and are markedly higher for Euro-American than for Asian
populations.

16

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE

Cumulative baseline risk (cases per 100)40
35Euro-American male

30

Asian male
Euro-American female

25

Asian female

20
15
10
5
0
0

5

520

Fig. 3.3. Cumulative baseline risk for all solid cancer incidence in reference populations.

Cumulative baseline risk (cases per 100)

521
522

1.0
0.9

Euro-American male

0.8

Asian male

0.7

Euro-American female

0.6

Asian female

0.5
0.4
0.3
0.2
0.1
0.0
0

523
524

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90
Attained age (years)5

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90
Attained age (years)

Fig. 3.4. Cumulative baseline risk for all non-CLL leukaemia incidence in reference populations.

525

17

Cumulative baseline risk (cases per 100)

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
12
Euro-American female

10

Asian female

8
6
4
2
0
0

526
527

5

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90
Attained age (years)

Fig. 3.5. Cumulative baseline risk for female breast cancer incidence in reference populations.

528
529

3.1.2. Risk models for radiation-associated cancers

530

3.1.2.1. Solid cancers

531
532
533
534
535
536
537
538
539
540
541
542
543
544

(35)Radiation-associated cancer risk models were developed for ten categories: nine organs
or tissues (oesophagus, stomach, colon, liver, lung, female breast, ovary, bladder, thyroid), anda set of other solid cancers (Table 3.1) using data from the analyses of solid cancer incidence
risk of the atomic bomb survivor Life Span Study (LSS) published in 2007 (Preston et al.,2007). These models considered cancer incidence data, with a follow-up from 1958 through1998. Risk estimates were adjusted to reduce the bias in risk estimates arising from uncertainty
in individual dose estimates derived from the dosimetry system 2002 (DS02). No specific risk
models were derived for brain and salivary glands.
(36)Risk models involved a linear dose response allowing for modifying effects of sex, age
at exposure, and attained age. These effects were constrained to equal the values obtained forall solid cancers as a group unless there were indications that these constraints resulted in amarked reduction in the goodness of fit when modelling cause-specific cancer types. Either theexcess relative risk (ERR) or excess absolute risk (EAR) was modelled.
(37)The model equation was as follows:!"#$%% '(%) = +. -. $"./01 2($ − 30)/109 + 02 <=(>/70)@

545
546
547
548
549

where d = dose (Gy)2, e = age at exposure (years) and a = attained age (years). Risk coefficientsused for radiation detriment calculation are summarised in Tables A.4.6 and A.4.7 in
Publication 103 (ICRP, 2007). See parameter values by sex, for ERR-based (Table 3.2) andEAR-based models (Table 3.3). The ERR/Gy and EAR/104 person-years/Gy for all solid2

The dose in Gy is intended to represent that of low LET radiations since DS02 organ dose estimatesin the reference (Preston et al., 2007) were calculated as the sum of the γ-ray dose plus 10 times theneutron dose to allow for the greater biological effectiveness of neutron doses.
18

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
550
551

cancers are illustrated in both sexes by age at exposure and attained age in Figs. 3.6 and 3.7,
respectively.

552
553
554
555
556

(38)The minimum latency period is the shortest time in which a specified radiation-induced
tumour is known or believed to occur after exposure. The minimum latency period used forsolid cancers in Publication 103 was five years.
Table 3.1. Risk models used for each organ/tissue category (ICRP, 2007).
Dose-risk
relationshipg
L

Risk transfer modelhc

LSS incidence

L

50%ERR:50%EAR

Colon

c

LSS incidence

L

50%ERR:50%EAR

Liver

LSS incidencec

L

50%ERR:50%EAR

Lung

c

Organ/tissue

SourceOesophagus

LSS incidencec

StomachLSS incidence

Bone
Skin

b

c
d
e
f
g
h

L

50%ERR:50%EAR

e

L

100%ERR

Breastc

LSS incidenceL

100%EAROvaryc

LSS incidence

L

50%ERR:50%EAR

Bladderc

LSS incidence

L

50%ERR:50%EAR

Thyroidc

L

100%ERRf

LSS incidence

LQ

50%ERR:50%EARc

LSS incidence

L

50%ERR:50%EAR

Gonads (heritable)

–

–

–

Brain

–

–

–

Salivary glands

–

–

–

Other solid

557
558
559
560
561
562
563
564
565
566
567
568

30%ERR:70%EAR

Nominal risk of ICRP 59

LSS incidence

Bone marrowaLd

Nominal risk of ICRP 60

a

50%ERR:50%EARb

Non-melanoma skin cancers.
Remainder tissues (14 in total): adrenals, extra-thoracic region, gall bladder, heart, kidneys,
lymphatic nodes, muscle, oral mucosa, pancreas, prostate, small intestine, spleen, thymus,
uterus/cervix.
LSS, incidence, 1958–1998, DS02 (Preston et al., 2007).
Mortality (ICRP, 1991).
Mortality (ICRP, 1992).
LSS, incidence, 1950–1998, DS02 (a special analysis of leukaemia data, unpublished).
L: linear; LQ: linear-quadratic.
EAR: excess absolute risk; ERR: excess relative risk; see Section 3.1.4 for details on the transfer ofrisk estimates across populations.

19

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
569
570

Table 3.2. Coefficients of the ERR-based models for solid cancers incidence (from Table A.4.6,
Publication 103 (ICRP, 2007)).

Cancer site
All Solid
Oesophagus
Stomach
Colon
Liver
Lung
BreastSex

ERR per Gy at
age 70 for exposure
at age 30 (b a)

M
F
M
F
M
F
M
F
M
F
M
F
F

0.35
0.58
0.40
0.65
0.23
0.38
0.68
0.33
0.25
0.40
0.29
1.36
0.87

Ovary

571
572
573

Age at exposure:% change in ERR per
decade increase(x b)

Power of attained age
by which the ERR
varies (a2 a)

–17%

–1.65

–17%

–1.65

–17%

–1.65

–17%

–1.65

–17%

–1.65

+17%

–1.65

0%

2.26

F
0.32
–17%
M
0.67
Bladder
–17%
F
1.10
M
0.53
Thyroid
–56%
F
1.05
M
0.22
Other
–34%
F
0.17
a
b and a2 are the parameters in the model equation of excess risk.
ba1 = ln (1 + x), where a1 is the parameter in the model equation of excess risk.

20

–1.65
–1.65
0.00
–1.65

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
574
575

Table 3.3. Coefficients of the EAR-based models for solid cancers incidence (from Table A.4.7,
Publication 103 (ICRP, 2007)).

Cancer site

All Solid
Oesophagus
Stomach
Colon
Liver
Lung
Breast

M
F
M
F
M
F
M
F
M
F
M
F

Excess cases
per 10,000 persons
per year per Gy at
age 70 for exposure
at age 30 (b a)
43.20
59.83
0.48
0.66
6.63
9.18
5.76
2.40
4.18
1.30
6.47
8.97

F

10.9

Sex

577
578
579

Power of attained
age by which
the EAR varies (a2 a)

–24%

2.38

64%

2.38

–24%

2.38

–24%

2.38

–24%

2.38

1%

4.25

–39%

Ovary

576

Age at exposure:% change in EAR
per decade increase
(x b)

3.5 c
1.0
2.38

F
1.47
–24%
M
2.00
Bladder
–11%
6.39
F
2.77
M
0.69
Thyroid
–24%
0.01
F
2.33
M
7.55
Other
–24%
2.38
F
10.45
a
b and a2 are the parameters in the model equation of excess risk.
ba1 = ln (1 + x), where a1 is the parameter in the model equation of excess risk.
c
The upper value represents the age effect before age 50 years and the lower is for age greater than 50.

21

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
6
Age at exposure
10 years

5

20 years
30 years

ERR/Gy440 years
50 years

3
2
1
0
20

30

40

50
60
Attained age (years)

70

80

580
581
582

Fig. 3.6. Modification of the ERR for all solid cancers by age at exposure and attained age.

140
Age at exposure
EAR/10,000 person-years/Gy120

10 years
20 years

100

30 years
40 years

80

50 years
60
40
20
0
20

583

30

40

50
60
Attained age (years)

70

80

584
585

Fig. 3.7. Modification of the EAR for all solid cancers by age at exposure and attained age.

586

3.1.2.2. Leukaemia

587
588
589
590

(39)Leukaemia risk estimates were based on LSS incidence data, with a follow-up from 1950
to 1998, using the DS02 dosimetry system. The EAR-based model was similar to that derived
from the LSS in 1994 (Preston et al., 1994), with a linear-quadratic dose response that allows
for effect modification by sex, exposure age, and time following exposure. The ERR estimates
22

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
591
592
593
594
595

were computed from the LSS leukaemia EAR-based model and from the LSS leukaemia
background rate, taking into account sex, age at exposure and attained age. However, theequations of the EAR-based and ERR-based models for leukaemia were not available.
(40)The minimum latency period used for leukaemia in Publication 103 (ICRP, 2007) was
two years.

596

3.1.2.3. Bone cancer

597
598
599
600
601
602
603

(41)The nominal risk estimate was taken from Publication 60 (ICRP, 1991) because there
was no LSS data available to derive a specific risk model, and other data sources were
extremely limited. The same nominal risk was applied to both males and females. It should be
noted that the ICRP risk estimate for bone cancer was based on average bone dose from radium224 while dosimetric models estimated doses to bone surfaces, using a radiation quality factorof 20. In Publication 103 (ICRP, 2007), the risk estimate based on the average bone dose wasused although its possible conservatism was recognised.

604

3.1.2.4. Skin cancer

605
606
607
608
609
610
611
612

(42)For non-melanoma skin cancer risks, it was judged that LSS derived models may not beadequate for a general population because of differences in risk related to skin pigmentation.
Therefore, the Commission used the nominal skin cancer risk estimate from Publication 59(ICRP, 1992). The same nominal risk was applied to both males and females. This estimate
was also used in Publication 60 (ICRP, 1991). In Publication 59, the risks have been estimatedusing an absolute and a constant relative risk model (with no modifying effects of age or timesince exposure), using both mortality and incidence data, based on epidemiological andexperimental results published up to 1990 (ICRP, 1992).

613

3.1.3. Lifetime excess risk

614

3.1.3.1. Method of calculation

615
616
617
618
619
620

(43)Several types of lifetime risk estimates can be used to calculate the risk, over a lifetime,
for an individual to develop, or die from, a specific disease. The lifetime risk used in
Publication 103 (ICRP, 2007) for the radiation detriment calculation is the risk of exposureinduced cancer incidence (REIC).
(44)The REIC cumulates the excess cases over the background rate of the unexposed
individuals. When exposed to dose d at age e, it is expressed in the formula:!"#$% (', )) = ,

5678

9:;

621
622
623
624
625
626
627
628
629

[./% (0|', )) − ./% (0)]4(0|', )))0

where µic(a|e,d) and µic(a) denote incidence rates for a specific cancer c at age a with andwithout exposure, respectively. L is a minimum latency period, and S(a|e,d) is the cancer-freesurvival probability. In Publication 103, amax was set to 90 years, and REICs were calculated
for ten solid cancer sites and leukaemia.
(45)The incidence rate for specific cancer after the exposure is calculated as:!"# (%|', )) = !"# (%) × [ 1 + 011"# (%|', ))]or!"# (%|', )) = !"# (%) + -./"# (%|', ))23

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
630
631
632
633

where ERRic(a|e,d) and EARic(a|e,d) are the excess relative risk and the excess absolute risk ofthe specific cancer.
(46)Using the Kaplan-Meier method, the cancer-free survival probability can be calculatedas:1!(#|%, ') = *[ 1 − .(/|%, ') ]
234

634
635
636

where µ(n|e,d) denotes the rate of developing any type of cancer or dying from causes otherthan cancer at age n. It can be described as:!(#|%, ') = !(#) − !+,- (#) + !/,- (#|%, ')

637
638
639
640
641
642

where µ(n) and µmac(n) are the all-cause mortality and the all-cancer mortality, respectively, atage n in the unexposed. µiac(n|e,d) is the all-cancer incidence at age n after exposed to dose dat age e, which is calculated as:!"#$ (&|(, *) = !"#$ (&) × [1 + 122"#$ (&|(, *)]or!"#$ (&|(, *) = !"#$ (&) + ./0"#$ (&|(, *)

643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660

where ERRiac(n|e,d) and EARiac(n|e,d) are the excess relative risk and the excess absolute riskof the all types of cancer.
(47)The risk models and survival function described above were used to compute sexspecific lifetime risk estimates for the Asian and Euro-American composite populations. For
each solid cancer site and for leukaemia, the considered exposure scenario was acute exposure
to 0.1 Gy. REIC at 1 Gy was computed as the REIC at 0.1 Gy multiplied by 10.
(48)Two nominal populations were considered: the whole population (age at exposure 0 to
84 years) and adult workers (age at exposure 18 to 64 years). REIC was calculated for each age
at exposure by cumulating the risk until the attained age reaches 90 years, as in Publication 60(ICRP, 1991). This means that the risk was cumulated over an age range 0–89 years (maximum
90 years) for the whole population, and 18–89 years (maximum 72 years) for adult workers.
(49)For the calculation of leukaemia lifetime risk, the risk models derived from the LSS
considered all leukaemia (including CLL), whereas the baseline reference rates from Asian andEuro-American populations considered non-CLL leukaemia. This difference has little impact
as CLL cases are very rare in Japan. Nevertheless, as the equations of the EAR-based and ERRbased models were not available for leukaemia, calculations of lifetime risk of leukaemia are
not presented in the rest of this report.

661

3.1.3.2. Age-dependence of lifetime excess risk

662
663
664
665
666
667
668
669
670
671

(50)Figs 3.8 and 3.10 show the excess risk of solid cancers, cumulated up to a given attained
age, in Euro-American females with a single exposure to 1 Gy at different ages at exposure (0,20 and 40 years), using an ERR-based and EAR-based model, respectively. Figs 3.9 and 3.11
show lifetime excess risk of solid cancers (up to the age of 89) in the general population witha single exposure to 1 Gy, using an ERR-based and EAR-based model, respectively.
(51)Figs 3.8–3.11 illustrate the change of the cumulative excess risk with respect to theattained age and age at exposure. No DDREF was applied at this step of calculation. The data
points shown by diamonds in Fig. 3.8 for a radiation exposure to 1 Gy at 0, 20 or 40 years ofage in Euro-American females correspond to those in Fig. 3.9. Similarly, the data points
marked by circles in Fig. 3.10 for a radiation exposure to 1 Gy at 0, 20 or 40 years of age in24

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694

Euro-American females correspond to those in Fig. 3.11. The cross markers in Figs. 3.8 and3.10 indicate the cumulative excess risk 20 years after the exposure.
(52)Figs 3.8 and 3.10 show that the cumulative excess risk increases gradually from 5 years
after exposure (reflecting the minimum latency period of 5 years) up to the age of 89 years.
This increase is due to the increase in the cumulative baseline risk. It should also be noted thatthe cumulative excess risk 20 years after exposure (represented by the crosses) is slightly higher
for exposure at the age of 40 years than at the age of 20 years and at the age of 0 year. This is
the result of the counter-balancing effects between the increase in the cumulative baseline risk
with attained age and the decrease in the risk coefficient with attained age for an ERR-basedmodel (Fig. 3.6), or with age at exposure for an EAR-based model (Fig. 3.7). The cumulative
excess risk at age 89 years is lower for exposure at age 40 years than at age 20 and 0 years; thisis due to the shorter remaining duration of life for older ages at exposure.
(53)Figs 3.9 and 3.11 show that the lifetime excess risk decreases gradually with age at
exposure from birth to the age of 85. This decrease is mainly due to the reduction of remaining
duration of life with increasing age at exposure, and also partly due to the decrease in the risk
coefficient with age at exposure. For age at exposure 85 years or more, the lifetime excess risk
is zero (due to the minimum latency period of 5 years). These figures also show the difference
between sexes and geographical regions. The lifetime excess risk is higher among females thanamong males. Using an ERR-based model, the lifetime excess risk is higher in the EuroAmerican population than in the Asian population (Fig. 3.9), whereas such difference is notapparent when the EAR-based model was used (Fig. 3.11). Nevertheless, the decrease of the
lifetime excess risk with age at exposure is similar in all populations.

Cumulative excess risk (cases per 100)50
Age at exposure40

0 year20 years
40 years

30

20

10

0
0
695
696
697
698

5

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90
Attained age (years)

Fig. 3.8. Cumulative excess risk at 1 Gy for all solid cancers in Euro-American females by age at
exposure, using an ERR-based model. The data points shown by diamonds correspond to those in Fig.
3.9. The cross markers indicate the cumulative excess risk 20 years after the exposure.

25

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
50

Lifetime risk (cases per 100)Euro-American male
40

Asian male
Euro-American female
Asian female

30

20

10

0
0

5

699
700
701
702

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90
Age at exposure (years)

Fig. 3.9. Lifetime excess risk for all solid cancers after exposure to 1 Gy, using an ERR-based model.
The data points shown by diamonds correspond to those in Fig. 3.8.

Cumulative excess risk (cases per 100)50
Age at exposure40

0 year20 years

30

40 years

20

10

0
0

5

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90
Attained age (years)

703
704
705
706
707

Fig. 3.10. Cumulative excess risk at 1 Gy for all solid cancers in Euro-American females by age at
exposure, using an EAR-based model. The data points marked by circles correspond to those in Fig.
3.11. The cross markers indicate the cumulative excess risk 20 years after the exposure.

26

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE

Lifetime risk (cases per 100)50Euro-American male

40

Asian male
Euro-American female

30

Asian female

20

10

0
0

5

708

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90
Age at exposure (years)

709
710

Fig. 3.11. Lifetime excess risk for all solid cancers after exposure to 1 Gy, using an EAR-based model.
The data points marked by circles correspond to those in Fig. 3.10.

711
712

3.1.3.3. Averaging lifetime excess risk

713
714
715
716
717

(54)The age-averaged lifetime excess risk was calculated as a weighted mean of REIC for
overall ages at exposure. The weight was assigned in proportion to the population of each age
group in the reference population as shown in Fig. 3.12, which illustrates the population
distribution by 5-year age categories for Asian and Euro-American populations.

27

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE

718
719

Fig. 3.12. Euro-American and Asian population size by age group.

720
721
722
723
724
725
726

(55)Table 3.4 summarises the averaged lifetime excess risk for solid cancers by site
calculated for the general population (0–89 years of age) with ERR-based and EAR-basedmodels, in Euro-American and Asian populations. They were calculated as an unweighted
mean of the lifetime excess risks for both sexes, each of which was the weighted mean of
REICs for ages at exposure of 0 to 84 years.

28

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
727

Table 3.4. Sex- and age-averaged lifetime excess risk for the whole population.

CancerOesophagus

Cases per 100 per Gyusing an ERR-based model
EuroAsian
American
0.24
0.43

Cases per 100 per Gyusing an EAR-based model
EuroAsian
American0.22
0.20

Stomach

0.27

1.61

2.08

2.38

Colon

1.34

1.73

1.01

1.20

Liver

0.11

0.90

0.67

0.80

Lung

3.05

2.60

1.77

1.84

Breast

–

–

2.04

2.45

Ovary

0.28

0.14

0.20

0.23

Bladder

1.34

0.66

0.60

0.63

Thyroid

0.49

0.73

–

–

Other solid

3.95

2.29

2.36

2.71

All solid

14.86

13.37

13.37

15.32

728
729

3.1.4. Transfer of risk estimates across populations

730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755

(56)It is problematic to transfer site-specific risk estimates of radiation-associated cancers
from one population to the other if the corresponding baseline rates differ. To address this issue,
the population risks were defined as weighted averages of the EAR- and ERR-based risk
estimates with weights based on judgements concerning the relative applicability of the tworisk estimates (Table 3.1). Weights of 0.5 were used for all tissues except the breast, thyroid,skin and lung.
(57)For female breast cancer, a pooled analysis of radiation effects (Preston et al., 2002)
provided evidence against the use of common ERR-based models. Therefore, female breast
cancer risks were based solely on an EAR-based model derived from recent incidence datafrom the LSS (Preston et al., 2007).
(58)For thyroid cancer, the use of EAR-based models appeared to be problematic because
variation in screening intensity has a marked effect on the rate of radiation-associated thyroid
cancers. Therefore, based on an analysis of radiation-associated thyroid cancer risks (Ron etal., 1995) and on the most recent available results from the LSS (Preston et al., 2007), thyroid
cancer risks were based solely on an ERR-based model. The same weighting scheme wasapplied to skin cancer as well.
(59)For lung cancer, the atomic bomb survivor data suggested that the EAR was morecomparable across sexes than the ERR, and also that radiation dose and smoking history
interacted additively as lung cancer risk factors (Pierce et al., 2003). Consequently, the ERRbased model was given a weight of 0.3 and the EAR-based model a weight of 0.7.
(60)For leukaemia, transfer to other populations was done using both EAR and ERR
estimates. The detriment computations used an average (50:50%) of the EAR and ERR transfer
risk estimates (a 100% EAR transfer was erroneously indicated in Publication 103 (ICRP,
2007)). Nevertheless, as the equations of the EAR-based and ERR-based models were notavailable for leukaemia, calculations of lifetime risks of leukaemia are not presented in the restof this report.
29

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
756
757(61)In summary, ERR:EAR weights of 0:100% were assigned for breast, 100:0% for thyroid
and skin, 30:70% for lung, and 50:50% for all others including leukaemia (Table 3.1).

758

3.1.5. Application of DDREF

759
760
761
762
763
764
765
766
767
768
769
770
771
772
773

(62)Experimental studies show that biological effectiveness of radiation exposure at low
doses and low dose rates is usually lower compared with exposures at high doses and high dose
rates, suggesting that dose-specific estimates based on high-dose, acute exposure data should
be divided by a DDREF for applications to low-dose, continuous, or fractionated exposures.
Recognising uncertainties, the Commission recommended in Publication 103 (ICRP, 2007)
that a DDREF of 2 continued to be used for radiological protection purposes. The Commission
stressed that its recommendation was a broad judgement including elements of bothsubjectivity and probabilistic uncertainty.
(63)The lifetime risk estimates were adjusted downward by a factor of 2 to account for a
DDREF, except for leukaemia for which the linear-quadratic dose-response model alreadytakes into account the risk modification at low doses. The same DDREF applied to males andfemales, the whole population and adult workers.
(64)The DDREF applies specifically to doses below 0.2 Gy or dose rates less than 0.1 Gy
per hour (ICRP, 1991). This means the radiation detriment assumes low-dose and/or low-doserate exposures.

774

3.1.6. Integration of heritable effects

775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795

(65)To estimate the risk of heritable effects, the relative importance of genetic components
as well as the frequency of transmissible mutations needs to be taken into account. The
UNSCEAR 2001 Report provided risks expressed as the predicted number of additional cases(i.e. over the baseline) of different classes of genetic disease per million live births per Gy fora population exposed to low-LET, low-dose or chronic irradiation, generation after generation(UNSCEAR, 2001). For all classes except congenital abnormalities, the estimates were basedon a doubling dose (DD) of 1 Gy and the respective values of baseline frequency, mutation
component and potential recoverability correction factor for the different classes of genetic
diseases. For congenital abnormalities, the risk estimate came from mouse data and was notbased on the DD method.
(66)On the basis of UNSCEAR (2001), the Commission derived ICRP estimates of risks for
all classes of genetic diseases: Mendelian diseases, chronic diseases and congenitalabnormalities (Tables A.6.4 and A.6.6, Publication 103 (ICRP, 2007)). While based on thestate of knowledge in this area, the strengths and limitations of these estimates need to be bornein mind, in view of various underlying assumptions.
(67)The Commission decided to use risk estimates for the first two generations (c.f. twogenerations in Publication 26 (ICRP, 1977a) and all generations in Publication 60 (ICRP,
1991)). The risk of heritable effects in the whole population associated with gonadal dose wasestimated to be around 20 cases per 10,000 people per Gy. The risk for adult workers wasestimated to be 60% of that for the whole population, leading to an estimated nominal risk of
12 per 10,000 per Gy. These values were applied to both males and females.

796

3.1.7. Nominal risk coefficient

797
798

(68)Following the steps mentioned above, the nominal risk coefficient was computed for 14
organs or tissues, which include 12 cancer sites (oesophagus, stomach, colon, liver, lung, bone,

30

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
799
800
801
802
803
804
805
806
807
808
809
810
811

skin, female breast, ovary, bladder, thyroid, red bone marrow), a set of the remaining cancer
sites grouped into one ‘remainder’ category, and the gonads for heritable effects.
(69)Some radiation-related cancers are sex-specific, and for many others, sex is a major
modifier of radiation-related risk. Nominal cancer risks were calculated separately for males
and females, and for the whole population and for adult workers (Tables A.4.18 and A.4.19,
Publication 103 (ICRP, 2007)).
(70)In accordance with ICRP procedures, intermediate and final numerical risk estimateshave been sex-averaged as an unweighted mean between male and female estimates. For
ovaries, the average was calculated considering that lifetime risk among males was zero. For
breasts, the average was calculated given that lifetime risk among males was zero. This
assumption was made because of the rare occurrence of male breast cancer. 3 Sex-averagenominal cancer risks for the whole population and for adult workers are presented in Table 3.5.

3Although a recent analysis of the LSS data (Brenner et al., 2018) suggested a significant positivedose response for male breast cancer, this assumption continues to be valid considering the very small
number of cases.
31

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
812
813

Table 3.5. Nominal risk coefficients in Publication 103 (ICRP, 2007): by sex for the general populationand for workers (from Tables A.4.1, A4.18 and A4.19).
Nominal risk coefficient (Ra)
Men
Women
Both sexes
Whole population (age 0–84 years at exposure)
Oesophagus
15
16
15
Stomach
68
91
79
Colon
91
40
65
Liver
41
19
30
Lung
76
153
114
Bone (surface)
7
7
7
b
Skin
1000
1000
1000
Breast
0
224
112
Ovary
0
21
11
Bladder
46
41
43
Thyroid
12
53
33
Bone marrowc
48
36
42
d
Other solid
157
131
144
Gonads (heritable)
20
20
20
Total
1580
1851
1715
Adult workers (age 18–64 years at exposure)
Oesophagus
14
16
16
Stomach
51
70
60
Colon
73
33
50
Liver
31
16
21
Lung
84
174
127
Bone (surface)
5
5
5
b
Skin
670
670
670
Breast
0
116
49
Ovary
0
16
7
Bladder
40
39
42
Thyroid
420
9Bone marrowc
24
22
23
Other solidd
94
88
88
Gonads (heritable)
12
12
12
Total
1103
1297 e1179
Organ/tissue

814
815
816
817
818
819
820
821
822

a
b
c
d

e

R is expressed in cases per 10,000 persons per Gy.
Non-melanoma skin cancers.
Non-CLL leukaemia.
Remainder tissues (14 in total): adrenals, extra-thoracic region, gall bladder, heart,kidneys, lymphatic nodes, muscle, oral mucosa, pancreas, prostate, small intestine,spleen, thymus, uterus/cervix.
This value corresponds to the sum of the above lines and is slightly different from that(1242) in ICRP Publication 103 (Table A.4.19).

32

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
823

3.2. Severity adjustment

824
825

(71)Table 3.6 summarises the parameters for severity adjustment by which the nominal risk
was converted into the radiation detriment.

826

3.2.1. Adjustment for lethality

827
828
829
830
831
832
833
834
835
836
837
838
839
840

(72)Since the nominal risk coefficient was calculated based on the excess incidence, thelethality fraction (k) was applied to take account of cancer severity.
(73)Lethality fractions were derived as judgement-based values reflecting the impact of
medical treatment for some types of cancer. In Publication 60 (ICRP, 1991), the choice of thevalues was based on the analysis of two sets of data from the US SEER programme: 5-year
survival rates by cancer site for 1980–1985 and 20-year survival rates for 1950–1970 (U.S.
DHHS, 1989). They were updated in Publication 103 (ICRP, 2007), but remained close to theprevious values. The same set of values were applied to males and females, the whole
population and adult workers.
(74)The lethality adjustment was performed by multiplying the nominal risk coefficient R
by the factor k. Highly lethal cancers received a relatively greater weight (e.g. 0.95 for livercancer, 0.89 for lung cancer) than those that seldom cause death (e.g. 0.002 for skin cancer,
0.07 for thyroid cancer) (Table 3.6).

33

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
841
842
843

Table 3.6. Construction of radiation detriment in Publication 103 (ICRP, 2007): from nominal risk
coefficient to radiation detriment for the whole population and for adult workers (from Tables A.4.1
and A.4.5).
NominalMin weight Non-fatal
Relative
Relative
Lethality
Radiationrisk
for non-fatalcasecancer freeradiation
fraction
detriment
coefficient
cancers
weightlife lost
detriment
*
*
R
k
qmin
q
l
D
Whole population (age 0–84 years at exposure)
Oesophagus
15
0.93
0.1
0.935
0.87
13.1
0.023
Stomach
79
0.83
0.1
0.846
0.88
67.7
0.118
Colon
65
0.48
0.1
0.530
0.97
47.9
0.083
Liver
30
0.95
0.1
0.959
0.88
26.6
0.046
Lung
114
0.89
0.1
0.901
0.80
90.3
0.157
Bone
7
0.45
0.1
0.505
1.00
5.1
0.009
Skinc
1000
0.002
0.0
0.002
1.00
4.0
0.007
Breast
112
0.29
0.1
0.365
1.29
79.8
0.139
Ovary
11
0.57
0.1
0.609
1.12
9.9
0.017
Bladder
43
0.29
0.1
0.357
0.71
16.7
0.029
Thyroid
33
0.07
0.2
0.253
1.29
12.7
0.022
d
Bone marrow
42
0.67
0.1
0.702
1.63
61.5
0.107
Other solide
144
0.49
0.1
0.541
1.03
113.5
0.198
Gonads
20
0.80
0.1
0.820
1.32
25.4
0.044
(heritable)
Total
1715
574
1.000
Adult workers (age 18–64 years at exposure)
Oesophagus
0.93
0.1
0.935
16
0.91
14.2
0.034
Stomach
0.83
0.1
0.846
60
0.89
51.8
0.123
Colon
0.48
0.1
0.530
50
1.13
43.0
0.102
Liver
0.95
0.1
0.959
21
0.93
19.7
0.047
Lung
0.89
0.1
0.901
127
0.96
120.7
0.286
Bone
0.45
0.1
0.505
5
1.00
3.4
0.008
Skinc
0.002
0.0
0.002
670
1.00
2.7
0.006
Breast
0.29
0.1
0.365
49
1.20
32.6
0.077
Ovary
0.57
0.1
0.609
7
1.16
6.6
0.016
Bladder
0.29
0.1
0.357
42
0.85
19.3
0.046
Thyroid
0.07
0.2
0.253
9
1.19
3.4
0.008
d
Bone marrow
0.67
0.1
0.702
23
1.17
23.9
0.057
Other solide
0.49
0.1
0.541
88
0.97
65.4
0.155
Gonads
12
0.80
0.1
0.820
1.32
15.3
0.036
(heritable)
Total
422
1.000
1179
*
R and D are expressed in cases per 10,000 persons per Gy and Sv, respectively.
q = k + qmin × (1 – k)
D = [(R × k) + (R × (1 – k) × q)] × l
Organ/tissue

844
845
846
847

34

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
848

3.2.2. Adjustment for quality of life

849
850
851
852
853
854
855
856
857
858
859
860
861
862
863

(75)Cancer survivors generally experience adverse effects on their quality of life. Thus, theCommission judged that cancers should be weighted not only by lethality but also for pain,
suffering, and any adverse effects of cancer treatment. To achieve this, a factor termed qmin wasapplied to the non-lethal fractions of cancers to produce a quality of life factor termed q. It is
expressed in a formula q = k + qmin × (1 – k), where k is the lethality faction and qmin is a tissuespecific constant representing the minimum weight for non-lethal cancers.
(76)qmin is a judgment-based parameter. The value of qmin was set equal to 0.1 except for theskin and thyroid. The qmin adjustment has an impact upon radiation detriment calculations inproportion to the fraction of cancers that are non-lethal. Accordingly, highly lethal cancers suchas lung and stomach cancer are little affected by qmin compared to less lethal cancers such asbreast or thyroid.
(77)No qmin adjustment was used for skin cancer because radiogenic skin cancers (i.e. nonmelanoma skin cancers) are almost exclusively of the basal cell type, which is usuallyassociated with very little pain, suffering or treatment sequelae. For thyroid cancer, qmin was
set to 0.2.

864

3.2.3. Adjustment for years of life lost

865
866
867

(78)To take into consideration the difference in the distribution of age at diagnosis amongcancer sites, the loss of life expectancy (LLE) was calculated for a specific cancer c by aformula:!!"# (%, ') = *

./01

234
./01

=7

./01

+(,|%)', − *.8234+# (,|%, ')',

234
./01

+(,|%) − 7.8234+# (,|%, ')

868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883

where the notations are the same as those in Section 3.1.3.1, and the cancer-free survival
probability Sc(a|e,d) allows for an alteration in the incidence of cancer c following radiation
exposure. The years of life lost for cancer c was given by dividing LLEc(e,d) by REICc(e,d), in
which the effect of dose d is cancelled out.
(79)Average years of life lost were computed for each sex in each composite population as
the weighted average over ages at exposure. These were converted to relative values (factor l)by division by the average years of life lost for all cancers. The average number of years of life
lost for all cancers was equal to 15 years as in Publication 60 (ICRP, 1991). The factor l reflects
the relative years of cancer-free life lost, with the value of less than 1 for cancers occurring late
in life (e.g. 0.71 for bladder cancer, 0.80 for lung cancer) and more than 1 for those occurring
early in life (e.g. 1.63 for red bone marrow, 1.29 for thyroid or breast cancer).
(80)The years of life lost for bone and skin cancer cannot be obtained in the same way andtherefore were arbitrarily set at the average years of life lost for all cancers. The value of l wastherefore equal to 1 for these two cancer sites. The gonads were assigned a value of 20 yearsof life lost on average for severe genetic disorders, which was equivalent to l of 1.32.

884

3.2.4. Calculation of radiation detriment

885
886

(81)As shown in Table 3.6, the radiation detriment D for each organ or tissue was calculated
by applying the above-mentioned factors to the nominal risk coefficient R using the formula:! = [$ × & + $ × (1 − &) × , ] × /

887

35

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
888
889
890
891
892

(82)The overall radiation detriment was calculated as an unweighted sum of the 14 tissuespecific detriments. The result is shown in terms of the number of cases per 10,000 persons per
Sv. It represents not the real number, but the weighted number of excess cases per unit dose ofradiation. ‘Sv’ is used to express the radiation dose since the radiation detriment is intended forthe purpose of radiological protection at low doses and low dose rates.

893

3.3. Relation between radiation detriment and effective dose: tissue
weighting factors wT

894
895
896
897
898
899
900
901
902
903

(83)The relative radiation detriments for the whole population, which are the normalised
radiation detriments of respective organs/tissues to sum to unity, form the basis of the tissue
weighting factors wT used for calculation of the effective dose. In Publication 60 (ICRP, 1991),
the Commission selected a very simplified system of weights, which used no more than fourgroups of weights and required no more than about a factor of 2 rounding between the relative
radiation detriments and the assigned weights. In Publication 103 (ICRP, 2007), the numericalvalues changed as shown in Table 3.7, but the basic concept remained unchanged.
Table 3.7. Tissue weighting factors used for each organ/tissue category in Publication 103 (ICRP, 2007).

Organ/tissue

wT

Oesophagus

0.023

0.034

0.04

Stomach

0.118

0.123

0.12

Colon

0.083

0.102

0.12

Liver

0.046

0.047

0.04

Lung

0.157

0.286

0.12

Bone

0.009

0.008

0.01

Skin

0.007

0.006

0.01

Breast

0.139

0.077

0.12

Ovary

0.017

0.016

Bladder

0.029

0.046

0.04

Thyroid

0.022

0.008

0.04

0.107

0.057

0.12

0.198

0.155

0.12

0.044

0.036

0.08

Brain

–

–

0.01

Salivary glands

–

–

0.01

1.000

1.000

1.00

Bone marrow
Other solid*

Gonads (heritable)

Total
904
905
906

Relative radiation
detriment
Whole
Adult
population
workers*

Remainder tissues (14 in total): adrenals, extra-thoracic region, gall bladder, heart, kidneys, lymphatic nodes,
muscle, oral mucosa, pancreas, prostate, small intestine, spleen, thymus, uterus/cervix.

36

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926

(84)The Commission has defined a single set of wT values that is applied to both sexes andall ages. Since the detailed relative radiation detriments in Table 3.6 and 3.7 were imprecise
because of uncertainties associated with their estimation, they were grouped into four
categories broadly reflecting the relative detriments.
(85)For the organs with the highest radiation detriments (lung, breast, stomach, red bonemarrow, colon, remainder tissues), the wT was set to 0.12. The gonads were assigned a wT of
0.08 based on the relative detriment for heritable effects and ovarian cancer. For the organs
with intermediate radiation detriments (bladder, oesophagus, liver, thyroid), the wT was set to
0.04. The wT value for the thyroid was set to 0.04 to take account of the concentration of cancer
risk in childhood (i.e. young children are considered to be a particularly sensitive subgroup for
thyroid cancer). For the organs with the lowest radiation detriments (skin, bone), the wT was
set to 0.01. Cancer risks in salivary glands and brain, whilst not specifically quantifiable, were
judged to be greater than that of other tissues in the remainder fraction and, for this reason,each was also assigned a wT of 0.01.
(86)A group of ‘remainder tissues’ was included to account for radiation detriments toorgans or tissues for which detailed radiation-risk calculations were uninformative. To makethe sum of wT equal to unity, the remaining value (0.12) was assigned to them. This category
denoted as ‘other solid cancers’ or ‘remainder tissues’ includes 14 organs or tissues, and thewT of 0.12 has to be considered as equally distributed between them.

927

37

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
928

4. SENSITIVITY OF RADIATION DETRIMENT CALCULATION

929
930
931
932
933
934
935

(87)Many parameters are involved in the calculation of the radiation detriment, and thevariation in the values adopted for these parameters can have effects on the total detriment,
which in turn could have implications on radiation protection practice. In order to examine theeffects of these variations on the radiation detriment, a sensitivity analysis was conducted fora variety of parameters. The analysis focuses on solid cancers other than bone and skin cancers.
(88)To reproduce the radiation detriment calculation as similarly as possible to that in
Publication 103, the following parameters were chosen:

936
937
938
939
940
941

•

The 100% ERR-based and the 100% EAR-based models were used for thyroid cancer risk
and breast cancer risk, respectively. A mixed model of 50% ERR-based and 50% EARbased was used for the rest of solid cancer risks, except for lung cancer where a model of
30% ERR-based and 70% EAR-based was used. For solid cancers, lifetime risk was
divided by a DDREF of 2 to take into account the effect from protracted radiation
exposure.

942
943

•

Population averaged lifetime risk with age at exposure of 0–84 years were calculated withattained age set at 89 years.

944
945

•

Nominal risks were calculated at 0.1 Sv, and then were linearly extrapolated to 1 Sv
through multiplication by a factor of 10.

946
947

(89)For the sensitivity analysis, the parameters were set differently from Publication 103 as
below and were changed one at a time to examine their impact on the radiation detriment.

948•

DDREF: 1.

949•

Age at exposure: 0–14, and 18–64 years.

950•

Sex: male and female, separately.

951•

Reference population: Euro-American and Asian, separately.

952•

Transfer model: 100% ERR and 100% EAR, separately.

953•

Minimum latency period for solid cancers: 10 years.

954•

Maximum attained age: 99 years.

955
956

•

Lifetime risk calculation method: lifetime attributable risk (LAR) and excess lifetime risk
(ELR).

957•

Lethality fraction: 1 for all cancer sites.

958•

Minimum quality of life factor: 0 for all cancer sites.

959•

Relative years of cancer-free life lost: 1 for all cancer sites.

960

4.1. Parameters involved in the calculation of the nominal risk

961
962
963
964
965

(90)In Publication 103 (ICRP, 2007), the nominal risk for solid cancers was divided by a
DDREF of 2 to take into account the possible effects of low-dose and low-dose-rate exposuresof the general population and workforce. However, the value of DDREF has become a topic ofdiscussion in recent years within the radiological protection community (Rühm et al., 2015,2016; Shore et al., 2017). The National Academy of Sciences-National Research Council
38

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
966
967
968
969
970

proposed a DDREF value of about 1.5 in BEIR VII (NRC, 2006), and some even consider thata DDREF of 1 should be used (SSK, 2014). The radiation detriment calculated with a DDREF
of 1 and 2 are presented in Fig. 4.1. As the radiation detriment is inversely proportional to the
DDREF, this leads to a difference of a factor of two for all solid cancers.

Detriment(cases/10,000 persons/Sv)

250
DDREF=2

200

DDREF=1

150
100
50

th
yr
oi
d
ot
he
rs
ol
id

de
r
bl
ad

ar
y
ov

br
ea
st

lu
ng

liv
er

lo
n
co

h

sto
m
ac

O

es
o

ph

ag
u

s

0

971
972

Fig. 4.1. Results of cancer detriment for DDREF values of 1 and 2. Taken from Zhang et al. (2020).

973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995

(91)The nominal risk was averaged over age-at-exposure 0–84 years in Publication 103(ICRP, 2007). Fig. 4.2. shows the radiation detriment for different groups of age at exposure.
Comparisons are made between three groups of age at exposure: 0–14 years, 18–64 years and
0–84 years which represent the young age population, working age population and whole
population, respectively. For most cancer sites, the detriment for the young ages-at-exposure
population (0–14 years) is higher than that for a whole population averaged (0–84 years). In
some cases (i.e. stomach cancer, breast cancer, thyroid cancer, and other solid cancer), thedetriment for 0–14 years is more than double compared with that for 0–84 years.
(92)The radiation detriment was also averaged over sexes and two composite populations
which were derived from four Asian and three Euro-American populations in Publication 103(ICRP, 2007). This methodology applied to all cancer sites, despite the fact that some cancer
incidences are higher in one population than others and some cancers are sex-specific, such as
ovary cancer and female breast cancer. Fig. 4.3 shows radiation detriment averaged over 0–84
years of age at exposure for the sexes separately, and Fig. 4.4 shows the radiation detriment forthe composite Euro-American and Asian populations separately. For lung cancer, the detrimentfor females appears to be higher than for males. For stomach and liver cancers, the detriment
for the Asian population appears to be higher than for the Euro-American population. For breastand ovary cancers, the differences in radiation detriments between the populations are
relatively small, but using the male detriment of zero reduces the overall detriment by 50%.
For other solid cancers, the detriment for the Euro-American male is somewhat higher thanthat for other population groups.

39

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE

Detriment(cases/10,000 persons/Sv)

300
0-84

250

18-64
0-14

200
150
100
50

996
997

ro
id
ot
he
rs
ol
id

th
y

bl
ad
de
r

ov
ar
y

t
br
ea
s

g
lu
n

liv
er

co
lo
n

ac
h
m

sto

O

es
op
h

ag
us

0

Fig. 4.2. Cancer detriment for different groups of age at exposure. Taken from Zhang et al. (2020).

998

Detriment(case/10,000 persons/Sv)

180
160

sex averaged

140

Male only

120

Female only

100
80
60
40
20

999
1000
1001

d
he
rs
ol
id

yr
oi

ot

th

er
ad
d
bl

ar
y
ov

ea
st
br

ng
lu

er
liv

lo
n
co

h
m
ac
sto

O
es
op

ha

gu
s

0

Fig. 4.3. Cancer detriment calculated for males and females separately, compared with sex averaged
values. Taken from Zhang et al. (2020).

1002

40

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
140
Population combined
Detriment(cases/10,000 persons/Sv)

120

EuroAm

100

Asian

80
60
40
20

th
yr
oi
d
ot
he
rs
ol
id

bl
ad
de
r

ar
y
ov

br
ea
st

lu
ng

liv
er

co
lo
n

O

es
o

ph
ag

us
sto
m
ac
h

0

1003
1004
1005

Fig. 4.4. Comparison of cancer detriment calculated for Euro-American and Asian populations. Taken
from Zhang et al. (2020).

1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032

(93)In Publication 103 (ICRP, 2007), the nominal risks were derived based on the 100%
ERR-based model for thyroid cancer, the 100% EAR-based model for breast cancer, the 30%
ERR-based + 70% EAR-based models for lung cancer, and the 50% ERR-based + 50% EARbased models for the rest of solid cancer sites (Table 3.1). This weighting scheme is related tothe different cancer baseline rates between populations and it would be problematic to transfer
radiation risk from one population to another using either the ERR-based or the EAR-basedmodel uniformly across all cancer sites. Therefore, the contribution to nominal risk from thetwo models varies depending on the cancer sites (see Section 3.1.4). Fig. 4.5 shows theradiation detriment calculated based on the 100% ERR-based and the 100% EAR-based models
for sex and ages-at-exposure averaged population in comparison with that derived using themethod described in Publication 103. As shown in Fig. 4.5, the contribution from the ERRbased model is relatively small for stomach cancer, liver cancer and relatively large for lungcancer, bladder cancer and other solid cancer.
(94)There is a minimum time required between induction of a cancer and its detection. Thislatent period is expected to differ with cancer site, but information is limited to only a few
cancers. There are uncertainties associated with this parameter as it can depend on the
diagnostic techniques available. The minimum latency period is considered to be 5–10 years
for solid cancer. Fig. 4.6 shows the comparison of radiation detriments using a different latency
period. The minimum latencies of 5 and 10 years produce little difference.
(95)As life expectancy increases, the cumulative lifetime radiation risk is also increasing,and this results in predicted additional deaths from radiation exposure. In this detrimentcalculation, the maximum attained age to 99 years instead of 89 years was used to examine its
impact on the radiation detriment calculation. Fig. 4.7 shows the comparison of cancer
detriments for the two different attained ages. The increase in radiation detriment with the
maximum attained age of 99 years depends on cancer sites, from 3% for the colon and liver, to
10% for the bladder.

1033

41

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
140
Combined models

Detriment(case/10,000 persons/Sv)

120

ERR only
100

EAR only

80
60
40
20

1034
1035
1036

id

ot

he
rs
ol

oi
d
yr
th

er
ad
d
bl

ar
y
ov

ea
st
br

ng
lu

er
liv

lo
n
co

ac
h
m
sto

O
es
op

ha

gu

s

0

Fig. 4.5. Radiation detriment calculated using a 100% ERR-based model and a 100% EAR-based model,
in comparison with the combined models used in Publication 103. Taken from Zhang et al. (2020).

1037
120

Detriment(case/10,000 persons/Sv)

100

latency=5y
latency=10y

80
60
40
20

1038
1039

ol
id

ot
he
rs

ro
id
th
y

bl
ad
de
r

ov
ar
y

br
ea
st

g
lu
n

liv
er

co
lo
n

O

es
o

ph
ag
us
sto
m
ac
h

0

Fig. 4.6. Variation in radiation detriment with different latency periods. Taken from Zhang et al. (2020).

1040

42

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
140

Detriment(case/10,000 persons/Sv)

120

Attained age=89
Attained age=99

100
80
60
40
20

1041
1042
1043

rs
ol

id

d
oi

ot
he

th
yr

de
r
bl
ad

ar
y
ov

ea
st
br

lu
ng

er
liv

n
co
lo

m
ac
h
sto

O
es
op
ha

gu
s

0

Fig. 4.7. Comparison of cancer detriment calculated for different maximum attained ages. Taken from
Zhang et al. (2020).

1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063

(96)The effects of varying the values of the parameters used in the radiation detrimentcalculation are summarised in Table 4.1. The second column of Table 4.1 shows the results
calculated based on the methodology of Publication 103 (ICRP, 2007) (hereafter referred to as‘standard detriment’) for various cancers, along with the ratio of radiation detriments undervarying conditions (those with the relevant parameter change). In reference to the detriment of
Publication 103, the ratios illustrate the sensitivity of the radiation detriment with respect tochanges in the value of the parameters used for the calculation. For example, the radiation
detriment from thyroid cancer in the group of 0–14 years of age at exposure is 3 times higher
than that of the group of 0–84 years of age-at-exposure. The detriment calculation for breast
cancer was based on the 100% EAR model, which does not depend on the baseline rate ofbreast cancer incidence. Although the baseline rate for breast cancer is higher for the EuroAmerican population than that of the Asian population, the detriment from radiation exposurefor the Asian population was higher than the Euro-American population as shown in Table 4.2.
This is because the radiation detriment is proportional to the product of the EAR model and thesurvival curve, while the EAR model produces the same results for the Euro-American and the
Asian population, the survival curve used in the calculation decreases more slowly for the
Asian population than for the Euro-American population between ages 50 and 75 years asshown in Fig. 3.2.

43

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE

Table 4.1. Standard detriment and ratio of radiation detriment for changed parameter values compared with standard detriment.

1064

Relative change in radiation detriment due to variation of input parameter values
Cancer siteStandard
detriment

Oesophagus

DDREF
=1

Age atexposure
18–64

Age atexposure
0–14

Male
only

Femaleonly

EuroAmerican

Asian

ERR-based
model only

EAR-based
model only

Latency =10 years

Attained age= 99 y

12.13

2

1.00

1.07

1.00

0.93

0.79

1.14

1.21

0.71

0.86

1.00

Stomach

67.71

2

0.80

1.86

0.86

1.15

0.73

1.27

0.59

1.42

0.96

1.05

Colon

48.44

2

0.85

1.79

1.39

0.61

0.89

1.09

1.15

0.83

0.95

1.03

Liver

27.22

2

0.81

1.84

1.35

0.65

0.65

1.35

0.81

1.19

0.97

1.03

Lung

83.88

2

1.16

1.01

0.67

1.33

1.01

0.99

1.34

0.86

0.92

1.08

Breast

78.93

2

0.58

2.52

0

2.02

0.92

1.10

0

1.00

0.98

1.05

Ovary

10.27

2

0.73

1.91

0

2.00

1.09

0.87

1.00

1.00

0.91

1.00

Bladder

15.52

2

0.93

1.55

1.08

0.95

1.20

0.80

1.25

0.78

0.98

1.10

Thyroid

12.32

2

0.32

3.23

0.29

1.71

0.77

1.19

1.00

0

0.97

1.03

Other solid
112.02
2
0.69
2.25
1.08
0.92
1.11
0.88
1.10
0.89
0.96
1.04
1065
Note: The first column is the standard cancer detriment derived for whole population 0–84 years. They were newly calculated for this report and are slightly different
1066
from the values in Table 3.6, which are quoted from Publication 103. The rest of columns represent the ratio of radiation detriment for the special condition defined in1067
the column title over the standard detriment in the first column. Taken from Zhang et al. (2020).
1068
1069

44

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
1070
1071
1072
1073
1074
1075
1076
1077

(97)Although the lifetime risk calculations in Publication 103 and this report were based on
the risk of exposure induced cancer/death (REIC/REID), there are variations with slightly
different methods (Thomas et al., 1992). Alternative methods are the lifetime attributable risk
(LAR) method or the excess lifetime risk (ELR) method. Comparisons of radiation detrimentbased on different lifetime risk calculation methods are shown in Fig. 4.8. There are small
differences (1–4%) in detriments for stomach, colon, liver, breast cancers, while the differences
become greater for other solid cancers (4–10%).

Detriment(case/10,000 persons/Sv)

120
LAR

100

REIC
ELR

80
60
40
20

r

oi
d
ot
he
rs
ol
id

th
yr

bl
ad
de

ar
y
ov

ea
st
br

lu
ng

er
liv

on
co
l

ac
h
sto
m

O
es
op
h

ag
u

s

0

1078
1079
1080
1081
1082

Fig. 4.8. Comparison of cancer detriment based on lifetime risk calculated by three different methods(LAR, REIC and ELR). Taken from Zhang et al. (2020).
LAR: lifetime attributable risk; REIC: risk of exposure induced cancer; ELR: excess lifetime risk

1083

4.2. Parameters related to adjustment for severity

1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098

(98)Apart from the nominal risks that are the most important part in the calculation ofradiation detriment, adjustment factors can also contribute to variation in the values of the
detriment. They include the lethality fraction (k), minimum quality-of-life factor (qmin), andrelative years of cancer-free life lost (l). The lethality fraction is used to compute the nominal
risk of fatal cancers, and at the same time, it serves as a parameter to adjust the quality of lifeof non-fatal cancers. The parameter qmin represents the minimum weight due to pain, suffering,
and any adverse effects of treatment that are commonly experienced by cancer survivors.
According to the formulation in Section 3.2.2, the adjustment factor q increases with the valuesof k and qmin, both of which are expressed as a value between 0 and 1. Setting qmin at the
maximum value (qmin = 1) produces the same effect as k = 1, demonstrating the worst level ofquality of life that is comparable to the loss of life (fatal cancers). In view of this relationship,potential impact of these parameters has been tested for two extreme scenarios k = 1 and qmin
= 0, respectively. The values of relative cancer-free life lost vary between organs and tissues,
ranging from 0.71 to 1.29 for solid cancers as shown in Table 3.6. To illustrate the effect ofchanges in this parameter, a calculation was made by setting l at 1 for every cancer site.
45

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
1099
1100
1101
1102
1103
1104
1105
1106
1107

(99)Fig. 4.9 shows the comparison between the radiation detriments calculated using themethodology described in Publication 103 (ICRP, 2007) and that with the lethality fraction
equal to one. For colon, breast, bladder, thyroid cancers and other solid cancer, there is a
noticeable reduction in radiation detriment using the lethality data from Publication 103,
compared to detriment using a lethality fraction equal to one. The Commission set the value ofqmin to 0.2 for thyroid cancer and 0.1 for other types of cancer in Publication 103. Fig. 4.10
shows the comparison between the use of these values and qmin of zero, showing that thedifferences in radiation detriment are small for most cancer sites, except for thyroid cancer, forwhich qmin of zero results in more than a 50% decrease in detriment.

1108

Detriment
cases/10,000 persons/Sv)

160
140

Detriment in ICRP 103

120

Detriment if k=1

100
80
60
40
20

1109
1110
1111

br
ea
st
ov
ar
y
bl
ad
de
r
th
yr
oi
d
ot
he
rs
ol
id

lu
ng

er
liv

co
lo
n

ac
h

st
om

Oe
so
ph
ag

us

0

Fig. 4.9. Comparison of standard cancer detriment with that calculated for lethality fraction (k) of one.
Taken from Zhang et al. (2020).

1112

1113
1114
1115

Fig. 4.10. Comparison of standard cancer detriment with that calculated for minimum quality-of-life
factor equal to zero. Taken from Zhang et al. (2020).

1116
1117

46

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
1118
120
Detriment(cases/10,000 persons/Sv)

Detriment in ICRP 103
100

Detriment if relative life lost=1

80
60
40
20

so
lid

r

d

ot
he
r

th
yr
oi

bl
ad
de

ov
ar
y

br
ea
st

lu
ng

er
liv

co
lo
n

ac
h

st
om

Oe
so
ph
ag

us

0

1119
1120
1121

Fig. 4.11. Comparison of standard cancer detriment with that for relative cancer-free life lost (l) equal
to one. Taken from Zhang et al. (2020).

1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142

(100) Fig. 4.11 shows the comparison of results using relative cancer-free life lost as
presented in Publication 103 (ICRP, 2007) and equal to one respectively. Variations inradiation detriment are particularly pronounced for breast, stomach and lung cancers, withincreases or decreases of up to about 30–40%.
(101) Table 4.2 summarises the ratios of radiation detriments over the standard detriment inrelation to the variations of the lethality fraction, the minimum quality-of-life factor and therelative cancer-free life lost parameters. The impact is noticeable for thyroid cancer when theminimum quality-of-life factor is set to zero. The radiation detriment varies for different cancer
sites. When the cancer-free life lost is set to be the same as that of all cancers combined,
detriment increases for some cancer sites, such as oesophagus and bladder, but decreases forother cancer sites, such as the breast.
(102) With the improvement in diagnostic techniques and treatment, the cancer death rate
has declined during recent decades. Publication of U.S. cancer statistics (Siegel et al., 2019)show that the cancer death rate has declined by 27% from 1991 to 2016. The decline is
pronounced in cancers with high lethality: in the case of lung cancer, the death rate has dropped
by 48% in men between 1990 and 2016, and by 23% in women from 2002 to 2016. The
situation may lead to a considerable change in the values of lethality fraction, and this should
be taken into consideration in the future. A more detailed discussion about this issue is foundin a study by Breckow et al (2018).

47

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
1143
1144

Table 4.2. Standard detriment and ratio of radiation detriment for different settings in lethality fraction,
minimum quality-of-life factor and relative years of cancer-free life lost, over the standard detriment.
Cancer siteStandard
detriment

Ratio of detrimentof lethality = 1 overstandard detriment

Ratio of detrimentof qmin = 0 overstandard detriment

Ratio of detriment ofrelative life lost = 1 overstandard detriment

Oesophagus

12.13

1.00

1.00

1.15

Stomach

67.71

1.03

1.00

1.14

Colon

48.44

1.32

0.96

1.03

Liver

27.22

1.00

1.00

1.14

Lung

83.88

1.01

1.00

1.25

Breast

78.93

1.83

0.91

0.78

Ovary

10.27

1.20

0.98

0.89

Bladder

15.52

1.83

0.91

1.41

Thyroid

12.32

3.24

0.44

0.78

Other solid

112.02

1.31

0.97

0.97

1145
1146
1147
1148

Note: The first column is the standard cancer detriment derived for whole population 0–84 years, as in Table
4.1 The rest of columns represent the ratio of radiation detriment for the special condition defined in thecolumn title over the standard detriment in the first column. Taken from Zhang et al. (2020).

1149

4.3. Summary of sensitivity analysis

1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172

(103) Based on the calculation result presented above, the parameters can be classified into
three categories according to their level of impact on radiation detriment: limited, noticeable
and large.
(104) Parameters of limited impact: minimum latency period, maximum attained age,
lifetime risk calculation method, minimum quality-of-life factor, and relative years of cancerfree life lost. Changing these parameters results in changes in radiation detriment by a factor
of less than 1.5. An exception is the minimum quality-of-life factor for thyroid cancer, but it
has little influence on the overall detriment.
(105) Parameters of noticeable impact: reference population and transfer model. Changing
the setting of these parameters shows changes in radiation detriment by a factor of 1.5 or more,
and less than 2 for some cancer sites. To transfer radiation risk from one population to another,both additive and multiplicative projections are plausible in terms of biological mechanism.
Nevertheless, for most cancer sites, the best way to transfer estimates of risk from radiation
exposure between populations is still unknown (UNSCEAR, 2012). The choice of the transfer
model is particularly important for cancers with varying baseline risks between populations. In
this regard, there is a significant difference in baseline rates between Asian and Euro-American
populations for female breast, stomach and liver cancer. Depending on the combination of thetransfer model and the population, radiation detriment can vary considerably for these cancers.
(106) Parameters of large impact: DDREF, age at exposure, sex and lethality fraction.
Changing the setting of these parameters demonstrates changes in radiation detriment by afactor of 2 or more for some cancer sites. The choice of DDREF value has a direct impact,
resulting in a two-fold increase in detriment for solid cancers when it is set to 1 instead of 2. Ina broad sense, the issue is not limited to the choice of the DDREF value, but is related to the48

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191

shape of the dose-response curve. UNSCEAR assumed a linear-quadratic dose-response
relationship in estimating the solid cancer risk instead of using the LNT model combined witha DDREF (UNSCEAR, 2006). As for the influence of sex and age at exposure, assuming afemale-only population doubles the radiation detriment for breast and ovarian cancers incomparison with the sex-averaged detriment; the exposure to children at 0–14 years of age
shows larger detriments, 3.2 times increase for the thyroid, 2.5 times for the breast and almost
double for several cancer sites. Finally, the lethality fraction can have a large impact on theradiation detriment. By increasing the lethality fraction to 1 results in a significant increase in
the detriment mainly for relatively non-lethal cancers such as thyroid, bladder and breast
cancers. Conversely, the progress in diagnostic techniques and treatments should bring about
a decrease in radiation detriment as of today and may lead to a significant decrease in the future.
(107) The sensitivity analyses presented here should be regarded as illustrative of the effectof the various factors involved in the calculation of radiation detriment. Bone cancer, skincancer and leukaemia were excluded from the analysis because of missing information to
perform calculations. The parameter settings were not necessarily realistic due to a paucity ofavailable data. For example, the lethality fraction and relative years of cancer-free life lost were
set to 1 and the minimum quality-of-life factor to 0, which oversimplifies the real-life scenarios.
Finally, the baseline mortality and incidence were as assumed in Publication 103 although theychanged over time.

1192

49

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
1193

5. POTENTIAL EVOLUTION

1194
1195
1196
1197
1198
1199
1200
1201
1202

(108) Radiation detriment is an indicator of the overall harm to health resulting from lowdose and low-dose-rate exposures. Based on scientific evidence, it takes into account key
aspects of human health and variability among sexes, ages and populations, forming a solid
basis for the system of radiological protection. Although the current scheme of radiation
detriment calculation is carefully designed to achieve this aim, it needs to evolve according toadvances in healthcare and scientific understanding of radiation health effects, as has been thecase in the past (see Section 2). There is also scope for further improvement in methodology.
In this section, the direction of future evolution and possible ways of improvement are
discussed.

1203

5.1. Input information

1204

5.1.1. Reference population data

1205
1206

(109) The calculation of the radiation detriment requires the use of reference population datafor baseline cancer rates, mortality and age- and sex-structure.

1207
1208
1209
1210
1211
1212
1213

•

Baseline rates used in Publication 103 (ICRP, 2007) correspond to the period 1993–1997(Parkin et al., 2002). Cancer incidence rates and mortality rates have changed significantly
since then due to the changes in lifestyle, advances in diagnostic methods andimprovement in cancer treatment, especially for certain cancers. Updating these reference
rates will provide a more realistic basis for the system of radiological protection in the
future (Breckow et al., 2018). Furthermore, no baseline rates were provided in Publication
103 for skin and bone cancers.

1214
1215
1216
1217
1218
1219
1220
1221

•

It should be noted that incidences and mortalities vary considerably around the worldreflecting genetic/lifestyle difference and differences in healthcare provision. In
Publication 103, two reference populations were considered: Asian (composite rates from
China (Shanghai), Japan (Osaka, Hiroshima and Nagasaki)) and Euro-American(composite rates from Sweden, United Kingdom and the Surveillance, Epidemiology and
End Results (SEER) program of the US National Cancer Institute). Extension to otherpopulations will provide a broader representation of the world population based on
available data.

1222

5.1.2. Cancer risk models

1223
1224
1225
1226

(110) The calculation of the radiation detriment requires the use of models describing therelationship between the organ/tissue dose and cancer risk for specific cancer sites. The
following points provide a summary of cancer risk models in Publication 103 (ICRP, 2007)
and possible ways of updating them.

1227
1228
1229
1230
1231

•

Radiation-associated cancer risk models for 11 categories of organs or tissues (oesophagus,
stomach, colon, liver, lung, female breast, ovary, bladder, thyroid, other solid cancers andred bone marrow) were derived from the LSS, based on a follow-up from 1958 through1998 (Preston et al., 2007). Since then, new models with longer follow-up have been
published, that can be used to update the risk models.

50

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
1232
1233
1234
1235
1236

•

For most solid cancers, risk modifying factors (age at exposure and attained age) wereparameterised as all solid cancers as a group, and the same values were used for both sexes.
The longer follow-up of the LSS will provide more detailed information to establish
models that better reflect the variation of risk with sex, age at exposure and attained age
for respective cancer sites.

1237
1238
1239

•

The bone marrow category includes leukaemia other than CLL. It is desirable to explore
the possibility of extending this category to other types of haematological malignancy,such as lymphoma and multiple myeloma.

1240
1241
1242
1243
1244

•

Nominal risk estimates for bone cancer and non-melanoma skin cancer were taken from
Publications 60 and 59 (ICRP, 1991, 1992), respectively. These risk estimates are basedon early studies, with large uncertainties, and do not allow for variation of risk with sex
and age. For better internal consistency in the calculation, it is desirable to investigate
whether more up-to-date risk models are available for these two tissues.

1245
1246
1247
1248

•

No specific risk models were derived for the brain and salivary glands, whereas tissue
weighting factors were assigned specifically to these two organs. To clarify the rationale
for these values, it is also desirable to explore the possibility of developing risk models
for these two organs.

1249
1250
1251
1252

•

The category ‘other cancer sites’ accounts for about 20% of the total radiation detriment.
When additional data are accumulated in the future, it is desirable to quantify the risk forsome of them as separate cancer sites in order to reduce the contribution of thisheterogeneous category.

1253
1254
1255
1256
1257
1258
1259

•

Most of the risk models were derived from the LSS without incorporating findings fromother sources. During the last decade, many reports provided risk models derived fromother epidemiological studies, especially for populations with protracted exposures (e.g.
nuclear workers, Mayak workers, residents along the Techa river, and Chernobyl cleanup workers). Evaluation of the models derived from these studies should be performed
based on a detailed analysis of their respective limitations and advantages, and discussionof the consistency of their results.

1260
1261
1262
1263
1264
1265
1266
1267
1268
1269

•

The models to calculate the nominal risks rely on several assumptions, including the LNTmodel, application of a DDREF, and the use of a transfer scheme based on the weightingof ERR and EAR models. The validity of these assumptions must be examined in the lightof the latest scientific findings. In this regard, recent epidemiological literature has beenreviewed by the National Council on Radiation Protection and Measurements to examine
the validity of the LNT model (NCRP, 2018; Shore et al., 2018, 2019). The Commission
has launched a Task Group to review the scientific basis of the DDREF in terms of
epidemiology, animal experiments and cell biology. Several papers have already been
published (Rühm et al., 2015, 2016, 2018; Shore et al., 2017; Tran and Little, 2017;
Wakeford et al., 2019) and a dedicated report will be released in due course.

1270

5.1.3. Cancer severity parameters

1271
1272

(111) Calculation of radiation detriment from nominal risks involves three parametersreflecting the severity of the diseases: lethality, quality of life, and years of life lost.

1273
1274
1275

•

Lethality fractions per cancer site have been provided as judgement-based values derived
from U.S. population data for the 1980–1985 and 1950–1970 periods (U.S. DHHS, 1989).
The same lethality fraction values were used for males and females, the general population
51

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE

and workers. Recent data exist, that provide much better estimates of current cancer
lethality, with variations with age and sex. Also, collection of lethality estimates fromother populations outside of the USA is desirable to better reflect variation of lethality
among different populations in the world.

1276
1277
1278
1279
1280
1281
1282
1283

•

Relative estimates of years of life lost were calculated from values used in Publication 60(ICRP, 1991). As in the case of the lethality fractions, a review of recent data sources will
provide better estimates of current years of life lost due to the specific cancers, withvariations with age and sex, and among different populations.

1284
1285
1286
1287
1288
1289
1290
1291

•

Adjustment for quality of life of cancer patients was based on the use of very approximate
value judgements. More elaborate approaches such as disability-adjusted life years(DALY) are now available to estimate and characterise the quality of life for a wide rangeof conditions (Chen et al., 2015; Shimada and Kai, 2015). A review of these methods andof available data can help, taking into account the variation with age, sex and geographicalregion. Some of these approaches combine the quality of life with lethality and years oflife lost indicators. Such methods should make the severity adjustment simpler and morereliable.

1292
1293
1294

•

The current scheme of the severity adjustment relies mainly on the lethality fraction, andthis method gives little weight to non-lethal cancers such as thyroid cancer. They would
be better handled if based on the characteristics of each type of cancer.

1295

5.1.4. Heritable effects

1296
1297
1298
1299
1300
1301
1302

(112) The risk of heritable effects in the radiation detriment is derived from the estimate in
the UNSCEAR 2001 Report for all classes of genetic diseases up to the first two generations(UNSCEAR, 2001). In recent years, new findings have been obtained, including epigenetic
inheritance. It is desirable to review the current literature on the mechanism of inheritance,
available data and the methods that can be used for estimating risks of heritable diseases.
Advances in this field should help integrate heritable effects into detriment calculation in amanner more consistent with the current methodology that was developed for cancer.

1303

5.2. Variation with sex and age

1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318

(113) The sensitivity analysis in Section 4 as well as the cancer risk estimate in Publication
XXX has demonstrated that the age at exposure has a large impact on radiation detriment. In
particular, an exposure during childhood substantially increases the lifetime risk for mostcancer sites compared to exposure during adulthood, which therefore results in a larger
calculated detriment value than that for adult exposure. Differences due to sex are also notable
for some tissues, with the most extreme examples of the ovary and the breast. It is advisable tocalculate lifetime risks separately for sexes and selected ages (age groups) and average in thelast stage to obtain a nominal value. While this approach requires the development of sex- andage-specific values at each step of the radiation detriment calculation (as far as possible) to
avoid averaging at intermediate steps, the results should allow to delineate the variation of risk
with sex, age at exposure and attained age, and potentially among different populations.
(114) This above approach distinguishes science-based risk assessment from the subsequent
integration of information for protection purposes, thus providing a better understanding of theconstruction of radiation detriment. This may also apply to other influential factors, including
modifiable lifestyle factors. The Commission defines the nominal population as a mixture of52

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333

people with different factors governing individual response to radiation. A new ICRP Task
Group has been set up to review scientific information relevant to the topic of individual
response. If factors that greatly influence the sensitivity to cancer induction are identified in
the future, whether or not modifiable, the variation of risk with them should be assessed.
(115) Considering situations where the radiation detriment is used for simplified risk
estimation, illustrating the variation of lifetime risk estimates with sex and age will help tounderstand potential deviations from individual risks in specific situations. This is especiallythe case in healthcare situations involving individual patients or specific groups of patients(Anderson et al., 2017).
(116) The current set of tissue weighting factors, wT was determined based on the sitespecific relative radiation detriments for the whole population (ICRP, 2007). Although therelative contribution of each cancer site to the detriment varies considerably with sex and age
at exposure, these variations were not presented in Publication 103. A detailed description ofthem, providing different sets of relative detriments, will help to understand the distribution
range and the representativeness of wT.

1334

5.3. Exposure scenario

1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355

(117) Lifetime risks are particularly high for childhood exposure, but the inclusion of adults
in the radiation detriment calculation dilutes and offsets the higher lifetime risks in children. A
similar situation could occur in a protracted exposure that lasts beyond young ages. The relative
contribution of childhood exposure to the total risk becomes smaller as years go by.
(118) With the use of DDREF, the radiation detriment for an acute exposure averaged over
the whole population is assumed to be equivalent to that for a lifelong continuous exposure of
an individual. Similarly, the radiation detriment of workers represents a constant occupational
exposure throughout the working life. While these two are the most typical patterns, otherexposure scenarios may be possible.
(119) In-utero exposure is not considered currently in the radiation detriment calculation. If
there is not much difference in cancer risk between antenatal exposure and childhood exposure,the lifetime risk for the foetus can be regarded comparable to that for the newborn. Thissuggests a limited impact on the nominal risk, but nevertheless, special consideration may beneeded from an ethical point of view.
(120) The sensitivity analysis demonstrated that length of life has little impact on theradiation detriment. Nevertheless, to reflect increased longevity in the recent decades,
extension of the lifetime beyond 90 years will be reasonable. Future demographic changes may
also have an impact on the detriment through the alteration of age distribution in the reference
populations.
(121) The dose for the radiation detriment calculation should continue to be 0.1 Gy todemonstrate it is intended for the low-dose, low-dose-rate exposure.

1356

5.4. Consideration of non-cancer effects

1357
1358
1359
1360
1361

(122) In Publication 118 (ICRP, 2012), the Commission made a comprehensive review ofaccumulating evidence that circulatory disease and cataracts might be induced at much lower
doses than previously considered, and therefore recommended a threshold dose of 0.5 Gy forthe heart, brain and the lens of the eye irrespective of dose rate. Some recent epidemiologicalstudies suggest a dose-dependent increase of the risk for these effects below 0.5 Gy, but there

53

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382

is considerable uncertainty about the shape of the dose-response curve at doses below this value
and the existence or not of a threshold (Baselet et al., 2016; NCRP, 2016).
(123) For circulatory disease, epidemiological data at low doses are varying according to
the health outcome considered and whether analyses are based on incidence or mortality(Yamada et al., 2004; Ozasa et al., 2016). Difficulties are also encountered in quantifying thebaseline risk. Health statistics on mortality from circulatory disease exhibit large variations
between countries and within each country over time. Data sources of morbidity or incidence
are limited and not as standardised as those for cancer. Adjustment for severity is notstraightforward, considering the large variation of symptoms and conditions of circulatory
disease among patients.
(124) For cataracts, evidence of the risk increase due to radiation exposure is morecompelling than circulatory disease. However, heterogeneity of epidemiological data isreported for lens opacities (NCRP, 2016), and the choice of the endpoint and diagnostic method
greatly influence the shape and slope of the dose-response curve. There is no reliable sourcefor baseline statistics on vision impairing cataracts. Furthermore, regional variation in healthcare development is a significant factor in adjusting for quality of life since cataract is a leading
cause of blindness in many developing countries where surgery is hardly accessible.
(125) In addition to the aspects discussed above, the determination of underlying biologicalmechanism and the identification of target tissues related to these effects should be clarified.
Whether or not to include them in the calculation of the radiation detriment currently remains
an open question.

1383

5.5. Transparency and comprehensibility

1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407

(126) As described in Section 3, the calculation of radiation detriment involves many steps
in which a wide range of information is processed, including risk models, health statistics andvarious other parameters. As the methodology becomes increasingly elaborate, it becomes
important to accurately document and publish the calculation procedure to ensure transparency
and traceability. A full description of the calculation steps is necessary, and it is desirable todevelop and share an open-source software to perform these calculations.
(127) Radiation detriment relates to stochastic effects and requires probabilistic
representation. In the current method, it takes the form of a risk value expressed as a percentage,
which is interpreted as the burden imposed on a nominal population. However, such a metricis difficult to understand for non-specialists. Another possible way is to express the detrimentin terms of the lengths of time lost from normal health and activity as a result of harmful effectsof radiation. This approach was taken in the assessment of the index of harm, which was
defined as years lost per 1000 worker-years (ICRP, 1977b, 1985). Expressing detriment basedon the expected values may give a wrong impression that the burden of disease is evenly
distributed in the population. Nevertheless, it is much more intelligible and applicable to any
deteriorated health condition. Indeed, a similar concept, DALY, is widely used in the fields ofwelfare, public health and medical services. It combines mortality and morbidity in a single
metric, and efforts have been made to assign reasonable weights to a wide range of non-fatal
conditions and impairments (Chen et al., 2015). This approach proved applicable to radiation
detriment as well (Shimada and Kai, 2015).
(128) There is no simple way to express the multidimensional nature of detriment, and it
will be necessary to improve its presentation in the future so that the make-up of radiation
detriment becomes more comprehensible to non-specialists. It is also desirable to provide
graphical presentation of key components of detriment, which will give a wider, balanced

54

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
1408
1409
1410

perspective on the health risks of radiation. They include information about reference
populations, absolute years of cancer-free life lost, and a baseline for the radiation detrimentcalculation (calculation assuming no radiation exposure).

1411

55

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE

6. SUMMARY AND CONCLUSIONS

1412

1413

6.1. Calculation of radiation detriment

1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429

(129) The concept of detriment was first introduced in Publication 22 (ICRP, 1973). In a
broad sense it includes any form of deleterious effects, but the methodology has been developed
to quantify the harmful health effects of radiation exposure at low doses and low dose rates. Its
principal components are probability of attributable cancer, probability of adverse heritableeffects, and weighting to adjust for the severity of these conditions. When the detriment iscalculated as an adjusted excess risk from radiation exposure using the Commission’s
methodology, it is specifically called radiation detriment.
(130) The calculation process of radiation detriment consists of two main parts. The firstpart is the calculation of nominal risks, which is an estimate of the lifetime risk of stochastic
effects averaged over both sexes, all ages at exposure and populations. The second part is thecalculation of the radiation detriment in which the nominal risk is adjusted for severity. The
second part is independent of radiation dose.
(131) Although Annex A of Publication 103 (ICRP, 2007) describes the data and models
for the radiation detriment calculation, the details were not fully documented. Section 3 of thisreport has provided full details of the detriment calculation procedure, clarifying the following
points.

1430
1431
1432
1433
1434

•

After verification, the risk transfer model for leukaemia turned out not to be 100% EAR
as indicated in Publication 103, but 50:50% ERR:EAR. The EAR-based model wasdeveloped using an LSS dataset with a follow-up from 1950 through 1998 and based on
the DS02 dosimetry system. The ERR-based model was derived from the EAR-basedmodel and the baseline risk, but details about the models are not available.

1435
1436
1437

•

The lifetime risk estimate was REIC, rather than ELR or LAR. It was cumulated over an
age range of 0–89 years (90 years of life) for the whole population, and 18–89 years (72years of life) for adult workers.

1438
1439

•

To estimate a lifetime risk per Gy, REIC at 0.1 Gy was calculated and multiplied by 10,
for each age at exposure.

1440
1441
1442
1443

•

The age-averaged lifetime risk was calculated as a weighted mean of the lifetime risk
estimated for each age-at-exposure, the weights being calculated using the age distribution
derived from the four reference populations (males and females of Asian and EuroAmerican populations).

1444

6.2. Sensitivity of radiation detriment

1445
1446
1447
1448
1449
1450
1451
1452

(132) In the calculation of radiation detriment, the lifetime risk estimates were adjusted
downward by applying a DDREF of 2 for solid cancer. The choice of the DDREF value thus
directly affects the detriment. For example, if the DDREF is set to 1, it doubles the radiation
detriment of solid cancers.
(133) Age at exposure and sex are influential factors as well. The radiation detriment for
the young age-at-exposure group (0–14 years) is higher than that for the whole population (0–84 years) by more than a factor of 2 for some cancer sites (i.e. stomach, breast, thyroid andother solid cancers). Sex-averaging results in a halving of risks from ovary and breast cancers.

56

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465

There are also significant differences in lifetime risk between males and females for otherorgans such as the lung, liver, colon and thyroid.
(134) The sensitivity analysis also demonstrated a large impact of the lethality fraction on
the radiation detriment. The lethality fractions currently used in the detriment calculation are
based on the data from the US in the 1980s. There is a need for updating these data as the
progress in diagnostic techniques and treatments since then may lead to a substantial decreasein lethality fraction.
(135) Another important result of the sensitivity analysis is the significance of the transfer
model. As was demonstrated in Section 4, the transfer model has a noticeable impact in thecalculation of radiation detriment. It is particularly important for cancers with varying baselinerisks between populations. The choice of the transfer model, together with the reference
populations, continues to be a fundamental issue in the estimation of radiation-associatedcancer risks and requires further research with updated information.

1466

6.3. Suggestions for future improvements

1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498

(136) Based on the result of the sensitivity analysis, DDREF, age at exposure and sex are
key factors to be considered to improve radiation risk estimation in the future. Efforts should
be made to better characterise the dose-response relationship for each cancer site at low doses
and low dose-rates. This may include promoting epidemiological studies of populations
exposed to chronic radiation exposure with good individual records, both for incidence andmortality data. More research is needed to refine the risk estimates for childhood exposures.
Elucidating the difference in sensitivity between males and females is another importantpriority.
(137) Considering the variation of cancer risk with sex and age, it is advisable to calculate
lifetime risks for both sexes and selected ages separately, and then to average them only in thelast stage to obtain a nominal value. This may also apply to other influential factors, including
modifiable lifestyle factors. If factors that greatly influence the sensitivity to cancer induction
are identified in the future, the variation of risk with them should be assessed.
(138) Age dependence of the risk also has relevance to the representativeness of the nominal
population. If a situation arises in which children and young people are mainly exposed, dueconsideration should be given to the validity of the radiation detriment for the whole population.
(139) Radiation detriment needs to evolve depending on changes in cancer incidence andsurvival rate, and on advances in scientific understanding of radiation health effects. From thisviewpoint, reference population data and cancer severity parameters need to be updated andimproved. There is also scope for improvement in cancer risk models, including use of the LSS
data with a longer follow-up, models derived from other epidemiological studies, especiallyfor populations with protracted exposures, and specific risk models for the bone, skin, brain,salivary gland and haematological malignancies other than leukaemia. Consideration of recent
findings regarding heritable effects of radiation is also necessary.
(140) The Commission recommended a lower threshold dose for circulatory disease andcataracts in Publication 118 (ICRP, 2012) than before, but there is considerable uncertainty
about the existence or not of a threshold for these effects and the dose response at low doses if
there is no threshold. Whether or not to include them in the calculation of the radiation
detriment currently remains an open question.
(141) As the methodology of detriment calculation changes, ensuring transparency andtraceability is important. A full description of calculation steps is necessary, and consideration
should be given to the development of an open-source software for calculating radiation

57

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
1499
1500
1501

detriment. It is also desirable to improve the presentation of the radiation detriment so that nonspecialists can have a balanced perspective on the health risks of radiation.

1502

58

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
1503

REFERENCES

1504
1505
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553

Baselet, B., Rombouts, C., Benotmane, A.M., et al., 2016. Cardiovascular diseases related to ionizing
radiation: The risk of low-dose exposure (Review). Int. J. Mol. Med. 38, 1623–1641.
Breckow, J., Emami, S., Amalhaf, S., et al., 2018. Impact of updating the non-radiation parameters in
the ICRP 103 detriment model. Radiat. Environ. Biophys. 57, 89–98.
Brenner, A.V., Preston, D.L., Sakata, R., et al., 2018. Incidence of breast cancer in the Life Span Studyof atomic bomb survivors: 1958–2009. Radiat. Res. 190, 433–444.
Chen, A., Jacobsen, K.H., Deshmukh, A.A., et al., 2015. The evolution of the disability-adjusted life
year (DALY). Socioecon. Plann. Sci. 49, 10–15.
Cléro, E., Vaillant, L., Hamada, N., et al., 2019. History of radiation detriment and its calculation
methodology used in ICRP Publication 103. J. Radiol. Prot. 39, R19–R35.
ICRP, 1973. Implications of Commission Recommendations that Doses be Kept as Low as Readily
Achievable. ICRP Publication 22. Pergamon Press, Oxford.
ICRP, 1977a. Recommendations of the International Commission on Radiological Protection. ICRP
Publication 26. Ann. ICRP 1(3).
ICRP, 1977b. Problems Involved in Developing an Index of Harm. ICRP Publication 27. Ann. ICRP
1(4).
ICRP, 1985. Developing a Unified Index of Harm. ICRP Publication 45. Ann. ICRP 15(3).
ICRP, 1991. The 1990 Recommendations of the International Commission on Radiological Protection.
ICRP Publication 60. Ann. ICRP 21(1–3).
ICRP, 1992. The Biological Basis for Dose Limitation in the Skin. ICRP Publication 59. Ann. ICRP
22(2).
ICRP, 2003. Relative Biological Effectiveness (RBE), Quality Factor (Q), and Radiation Weighting
Factor (wR). ICRP Publication 92. Ann. ICRP 33(4).
ICRP, 2007. The 2007 Recommendations of the International Commission on Radiological Protection.
ICRP Publication 103. Ann. ICRP 37(2–4).
ICRP, 2010. Lung Cancer Risk from Radon and Progeny and Statement on Radon. ICRP Publication
115, Ann. ICRP 40(1).
ICRP, 2012. ICRP Statement on Tissue Reactions / Early and Late Effects of Radiation in Normal
Tissues and Organs – Threshold Doses for Tissue Reactions in a Radiation Protection Context. ICRP
Publication 118. Ann. ICRP 41(1/2),
NCRP, 1985. Induction of Thyroid Cancer by Ionizing Radiation. NCRP Report No. 80. National
Council on Radiation Protection and Measurements, Bethesda, Maryland.
NCRP, 2016. Guidance on radiation dose limits for the lens of the eye. NCRP Commentary No. 26.
National Council on Radiation Protection and Measurements, Bethesda, Maryland.
NCRP, 2018. Implications of recent epidemiologic studies for the linear-nonthreshold model andradiation protection. NCRP Commentary No. 27. National Council on Radiation Protection andMeasurements, Bethesda MD.
NRC, 1990. Health Effects of Exposure to Low Levels of Ionizing Radiation: BEIR V Committee on
the Biological Effects of Ionizing Radiation (BEIR V), National Research Council. National
Academy of Sciences.
NRC, 2006. Health Risks from Exposure to Low Levels of Ionizing Radiation: BEIR VII – Phase 2,
National Research Council. National Academy of Sciences.
Ozasa, K., Takahashi, I., Grant, E.J., 2016. Radiation-related risks of non-cancer outcomes in the atomic
bomb survivors. Ann. ICRP 45 (1 Suppl.), 253–261.
Parkin, D.M., Whelan, S.L., Ferlay, J., et al. (Eds.), 2002. Cancer Incidence in Five Continents Vol VIII.
IARC Scientific Publications No. 155. Lyon International Agency for Research on Cancer.
Pierce, D.A., Sharp, G.B., Mabuchi K., 2003. Joint effects of radiation and smoking on lung cancer risk
among atomic bomb survivors. Radiat. Res. 159, 511–520.
Preston, D.L., Kusumi, S., Tomonaga, M., et al., 1994. Cancer incidence in atomic bomb survivors.
Part III. Leukaemia, lymphoma and multiple myeloma, 1950–1987. Radiat. Res. 137, S68–S97.

59

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
1554
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
1599
1600
1601
1602

Preston, D.L., Mattsson, A., Holmberg, E., et al., 2002. Radiation effects on breast cancer risk: a pooled
analysis of eight cohorts. Radiat. Res. 158, 220–235.
Preston, D.L., Ron, E., Tokuoka, S., et al., 2007. Solid cancer incidence in atomic bomb survivors:
1958–98. Radiat. Res. 168, 1–64.
Ron, E., Lubin, J.H., Shore, R.E., et al., 1995. Thyroid cancer after exposure to external radiation: apooled analysis of seven studies. Radiat. Res. 141, 259–277.
Rühm, W., Woloschak, G.E., Shore, R.E., et al., 2015. Dose and dose-rate effects of ionizing radiation:a discussion in the light of radiological protection. Radiat. Environ. Biophys. 54, 379–401.
Rühm, W., Azizova, T.V., Bouffler, S.D., et al., 2016. Dose-rate effects in radiation biology andradiation protection. Ann. ICRP 45(1S), 262–279.
Rühm, W., Azizova, T., Bouffler S., et al. 2018. Typical doses and dose rates in studies pertinent toradiation risk inference at low doses and low dose rates. J. Radiat. Res. 59 (suppl 2), 1–10.
Shimada, K., Kai, M., 2015. Calculating disability-adjusted life years (DALY) as a measure of excess
cancer risk following radiation exposure. J. Radiol. Prot. 35, 763–775.
Shore, R., Walsh, L., Azizova, T., et al., 2017. Risk of solid cancer in low dose-rate radiation
epidemiological studies and the dose-rate effectiveness factor. Int. J. Radiat. Biol. 93, 1064–1078.
Shore, R.E., Beck, H.L., Boice, J.D. Jr, et al., 2018. Implications of recent epidemiologic studies for the
linear nonthreshold model and radiation protection. J. Radiol. Prot. 38, 1217-1233.
Shore, R.E., Beck, H.L., Boice, J.D. Jr., et al., 2019. Recent epidemiologic studies and the linear nothreshold model for radiation protection–considerations regarding NCRP Commentary 27. HealthPhys. 116, 235–246.
Siegel, R.L., Miller, K.D., Jemal, A., 2019. Cancer Statistics, 2019. CA Cancer J Clin. 69, 7–34.
SSK, 2014. Dose- and dose-rate-effectiveness factor (DDREF), Recommendation by the German
Commission on Radiological Protection with Scientific Grounds.
Thomas, D., Darby, S., Fagnani, F., et al., 1992. Definition and estimation of lifetime detriment fromradiation exposures: principles and methods. Health Phys. 63, 259–272.
Tran, V., Little, M.P., 2017. Dose and dose rate extrapolation factors for malignant and non-malignant
health endpoints after exposure to gamma and neutron radiation. Radiat. Environ. Biophys. 56, 299–
328
UNSCEAR, 1982. Ionizing Radiation: Sources and Biological Effects. United Nations ScientificCommittee on the Effects of Atomic Radiation 1982 Report to the General Assembly, with Annexes.
New York: United Nations.
UNSCEAR, 1988. Sources, Effects and Risks of Ionizing Radiation. United Nations ScientificCommittee on the Effects of Atomic Radiation 1988 Report to the General Assembly, with Annexes.
New York: United Nations.
UNSCEAR, 2001. Heritable Effects of Radiation. United Nations Scientific Committee on the Effectsof Atomic Radiation 2001 Report to the General Assembly with Scientific Annex. New York: United
Nations.
U.S. DHHS, 1989. Cancer Statistics Review 1973-86 including a report on the status of cancer control.
NIH Publication No. 89-2789. US Department of Health and Human Services, PHS. NIH, NCI,
Bethesda, Maryland.
Wakeford, R., Azizova, T., Dörr, W., et al., 2019. The Dose and Dose-Rate Effectiveness Factor(DDREF). Health Phys. 116, 96–9
Yamada, M., Wong, F.L., Fujiwara, S., et al., 2004. Noncancer disease incidence in atomic bomb
survivors, 1958-1998. Radiat. Res. 161, 622–632.
Zhang, W., Laurier, D., Cléro, E., et al., 2020. Sensitivity analysis of parameters and methodologicalchoices used in calculation of radiation detriment for solid cancer. Int. J. Radiat. Biol. DOI:
10.1080/09553002.2020.1708499.

1603

60

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCEABBREVIATION LIST

1604
1605
1606

BEIR: Biological Effectiveness of Ionizing Radiation

1607

CLL: chronic lymphocytic leukaemia

1608

D: detriment1609

DALY: disability-adjusted life years

1610

DD: doubling dose

1611

DDREF: dose and dose-rate effectiveness factor

1612

DS: dosimetry system

1613

EAR: excess absolute risk

1614

ELR: excess lifetime risk

1615

ERR: excess relative risk

1616

DHHS: Department of Health and Human Services

1617

Gy: Gray1618

ICRP: International Commission on Radiological Protection1619

k: lethality adjustment factor

1620

l: years of life lost adjustment factor

1621

LAR: lifetime attributable risk

1622

LET: linear energy transfer

1623

LSS: life span study1624

LQ: linear-quadratic

1625

NCRP: National Council on Radiation Protection and Measurements

1626

NRC: National Research Council

1627

q: quality of life adjustment factor

1628

R: nominal risk coefficient

1629

REIC / REID: risk of exposure-induced cancer incidence / death

1630
1631

rem: Röntgen Equivalent Man, old unit of dose measuring the equivalent dose and effective dose (1rem = 0.01 Sv)

1632

SEER: Surveillance, Epidemiology, and End Results

1633

Sv: Sievert

1634

UNSCEAR: United Nations Scientific Committee on the Effects of Atomic Radiation

1635

WHO: World Health Organization1636

wT: tissue weighting factor

1637

YLL: years of life lost

1638

61

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCEGLOSSARY

1639
1640
1641

1642

Absorbed dose, D
The fundamental dose quantity given by:
D=

de
dm

1643
1644
1645

where d!̅ is the mean energy imparted to matter of mass dm by ionising radiation. For
radiological protection purposes, the absorbed dose DT, averaged over the organ or
tissue T, is often used, which is given by:

1646

#$ =

1647
1648
1649

where mT is the mass of the organ or tissue T, D is the absorbed dose in the masselement dm, and εT is the mean total energy imparted in the organ or tissue T. The SI
unit for absorbed dose is joule per kilogram (J kg–1) and its special name is gray (Gy).

1650

Active (red) bone marrow
The organ system bone marrow contains the cell systems for the formation of blood
cells starting from the pluripotent haematopietic stem cells to the mature blood cells.

1651
1652
1653
1654
1655

1
!$
( # d' ='$'$Baseline rates
The annual disease incidence observed in a population in the absence of expo- sure to
the agent under study.

1657
1658
1659
1660
1661
1662

Deterministic effectInjury in populations of cells, characterised by a threshold dose and an increase in theseverity of the reaction as the dose is increased further. The term means ‘causally
determined by preceding events’ in contrast to ‘stochastic effect’. In some cases,
however, deterministic effects are modifiable by post-irradiation procedures including
biological response modifiers. The more directly descriptive term ‘tissue reaction’ isalso used for this reason.

1663

Detriment (See ‘radiation detriment’).

1664

Disability-adjusted life years, DALY
A metric to quantify the burden of disease from mortality and morbidity. It is calculated
as the sum of the years of life lost (YLL) due to premature mortality in the populationand the years lost due to disability (YLD) for people living with the health condition orits consequences.

1656

1665
1666
1667
1668
1669
1670
1671
1672
1673
1674

Dose and dose-rate effectiveness factor, DDREF
A judged factor that adjusts biological effectiveness (per unit of dose) of radiation
exposures at low doses and low dose rates as compared with exposures at high doses
and high dose rates. The DDREF applies specifically to doses below 0.2 Gy or dose
rates less than 0.1 Gy per hour.

62

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
1675
1676
1677
1678
1679
1680
1681
1682
1683

Dose limit
The value of the effective dose or the equivalent dose to individuals from planned
exposure situations that shall not be exceeded.
Doubling dose, DD
The dose of radiation (Gy) that is required to produce as many heritable mutations as
those arising spontaneously in a generation.
Effective dose, E
The tissue-weighted sum of the equivalent doses in all specified tissues and organs of
the body, given by the expression:

E = å wT å wR DT,R = å wT H T
T

1684
1685
1686
1687
1688
1689
1690
1691

1692
1693
1694
1695
1696
1697
1698
1699
1700
1701
1702
1703
1704
1705

R

T

where HT or wR DT,R is the equivalent dose in a tissue or organ, T, and wT is the tissue
weighting factor. The unit for the effective dose is the same as for absorbed dose, J kg–
1, and its special name is sievert (Sv).
ELR
See ‘Lifetime risk estimates’.
Equivalent dose, HT
The dose in a tissue or organ T given by:

H T = å wR DT,R
R

where DT,R is the mean absorbed dose from radiation R in a tissue or organ T, and wR
is the radiation weighting factor. Since wR is dimensionless, the unit for the equivalentdose is the same as for absorbed dose, J kg–1, and its special name is sievert (Sv).
Excess absolute risk
The rate of disease incidence or mortality in an exposed population minus thecorresponding disease rate in an unexposed population. The excess absolute risk isoften expressed as the additive excess rate per Gy or per Sv.
Excess relative risk
The rate of disease in an exposed population divided by the rate of disease in an
unexposed population, minus 1.0. This is often expressed as the excess relative risk per
Gy or per Sv.
Gray (Gy)The special name for the SI unit of absorbed dose: 1 Gy = 1 J kg–1.

1706
1707
1708
1709
1710

Incidence (incidence rate)
The rate of occurrence of a disease in a population within a specified period of time,often expressed as the number of new cases of a disease arising per 100,000 individuals
per year (or per 100,000 person-years).

63

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
1711

LAR
See ‘Lifetime risk estimates’.

1712
1713
1714
1715
1716
1717
1718
1719
1720
1721
1722
1723
1724
1725
1726
1727
1728
1729
1730
1731
1732
1733
1734
1735
1736
1737
1738
1739
1740

Lethality fraction
Unitless judgement-based factor reflecting, for a cancer of specific organ or tissue, theratio between mortality and morbidity.
Life Span Study, LSS
The long-term cohort study of health effects in the Japanese atomic bomb survivors in
Hiroshima and Nagasaki.
Lifetime risk estimates
Estimates of the risk, over a lifetime, that an individual will develop, or die from, aspecific disease caused by an exposure. Several types of lifetime risk estimates can beused: 1) the excess lifetime risk (ELR) which is the difference between the proportionof people who develop or die from the disease in an exposed population and thecorresponding proportion in a similar population without the exposure; 2) the risk of
exposure-induced cancer death (REID) which is defined as the difference in a causespecific death rate for exposed and unexposed populations of a given sex and a given
age at exposure, as an additional cause of death introduced into a population; 3) the riskof exposure-induced cancer incidence (REIC) which replaces the cause-specific death
rate in the REID calculation with a cancer incidence rate; 4) loss of life expectancy(LLE) which describes the decrease in life expectancy due to the exposure of interest;
and 5) lifetime attributable risk (LAR) which is an approximation of the REID (orREIC) and describes excess deaths (or disease cases) over a follow-up period withpopulation background rates determined by the experience of unexposed individuals.
Linear dose response
A statistical model that expresses the risk of an effect (e.g. disease or abnormality) as
being proportional to dose.
Linear-non-threshold (LNT) model
A dose-response model which is based on the assumption that, in the low dose range,
radiation doses greater than zero will increase the risk of excess cancer and/or heritabledisease in a simple proportionate manner.

1742
1743
1744

Linear-quadratic dose response
A statistical model that expresses the risk of an effect (e.g. disease, death, orabnormality) as the sum of two components, one proportional to dose (linear term) andthe other proportional to the square of dose (quadratic term).

1745

LLE

1741

1746

See ‘Lifetime risk estimates’.

1748

Mendelian diseases
Heritable diseases attributable to single-gene mutations.

1749

Nominal risk coefficient

1747

64

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
1750
1751
1752
1753
1754
1755
1756
1757
1758
1759
1760
1761
1762
1763
1764
1765
1766
1767
1768
1769
1770
1771
1772
1773
1774
1775
1776
1777
1778
1779
1780
1781
1782
1783

Sex-averaged and age-at-exposure-averaged lifetime risk estimates for an organ or
tissue for a representative population. It is quantified assuming a linear-non-threshold(LNT) dose-response relationship for stochastic effects and applying a dose and doserate effectiveness factor (DDREF) of 2 for solid cancer.
Non-cancer diseases
Somatic diseases other than cancer, e.g. circulatory diseases and cataracts.
Quality of life factor
Unitless judgement-based factor representing adverse effects experienced by cancer
survivors on their quality of life, in terms of pain, suffering, and any adverse effects ofcancer treatment.
Radiation detriment
The concept of radiation detriment has been developed by the Commission for the
purpose of radiological protection. It is defined as the excess of stochastic health effects
in a group exposed to low-level radiation and its descendants compared to a nonexposed group. It is determined from nominal risk coefficients for a set of organs andtissues, taking into account the severity in terms of lethality, quality of life, and yearsof life lost.
Radiation weighting factor, wR
A dimensionless factor by which the organ or tissue absorbed dose is multiplied toreflect the higher biological effectiveness of high-LET radiations compared with lowLET radiations. It is used to derive the equivalent dose from the absorbed dose averaged
over a tissue or organ.
Relative years of life lost
The years of life lost (YLL) represent an average shortening of life expectancy in years
among those developing a cancer due to radiation exposure in comparison with anominal value for the unexposed. The relative years of life lost is the ratio of YLL due
to a cancer of a specific organ or tissue to YLL that is averaged over all cancer sites.
For the incidence-based calculations, years of cancer-free life lost are used instead of
YLL.
REIC/REID
See ‘Lifetime risk estimates’.
Sievert (Sv)
The special name for the SI unit of equivalent dose, effective dose, and operational dosequantities. The unit is joule per kilogram (J kg–1).

1785
1786
1787

Stochastic effects of radiation
Health effects for which the probability of occurrence in a population, but not theseverity, is regarded as a function of dose without threshold. Stochastic effects
contributing to radiation detriment are cancers and heritable effects.

1788

Threshold dose for tissue reactions

1784

65

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
1789
1790
1791
1792
1793
1794
1795
1796
1797
1798
1799
1800
1801
1802

Absorbed dose value (in Gy) to an organ or tissue below which it is considered that theincidence of tissue reactions in a population is less than 1%.
Tissue reaction
See ‘Deterministic effect’.
Tissue weighting factor, wT
The factor by which the equivalent dose in a tissue or organ T is weighted to represent
the relative contribution of that tissue or organ to the total radiation detriment resulting
from uniform irradiation of the body (ICRP 1991b). It is weighted such that:

åw =1
T

T

Transfer of risk (also called transport of risk)
Taking a risk model estimated for one population and applying it to another population
with different characteristics. Usually, the transfer mode is multiplicative (based on an
excess relative risk model), additive (based on an excess absolute risk model), or aweighted average of them.

1803

66

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
1804

ACKNOWLEDGEMENTS

1805
1806
1807
1808
1809
1810
1811
1812
1813
1814
1815
1816
1817
1818
1819
1820
1821
1822
1823
1824
1825
1826
1827
1828
1829
1830
1831
1832
1833
1834
1835
1836
1837
1838
1839
1840
1841
1842
1843
1844
1845
1846
1847
1848
1849
1850
1851
1852
1853
1854
1855

ICRP Task Group 102 was established in March 2016 to form a basis for future recommendations, byreviewing and documenting the current process of detriment calculation so that it may be carried out ina reproducible manner, considering ways in which different approaches might be applied when new
data become available. This report was prepared by Task Group 102 (a) to prepare a document
explaining the detailed procedure of detriment calculation and identifying sources for the necessary
information; (b) to reproduce the calculation in Publication 103, identify any difficulties in reproducing
the results, and comment on the approaches taken; (c) to identify potential modifications andimprovements in the detriment calculation procedures; and (d) to establish and propose a methodology
for future detriment calculation.
ICRP thanks all those involved in the development of this publication for their hard work and dedication
over many years.
Task Group 102 members (2016–2019)
N. Ban (Chair)
W. Dörr (–2019)
D. Laurier
L. Vaillant
W. Zhang

T.V. Azizova*
S. Bouffler*
E. Cléro*D.A. Cool*
N. Hamada*

J.D. Harrison*
D.L. Preston** Corresponding members
Committee 1 critical reviewers
M. Hauptmann

S. Salomaa

Main Commission critical reviewers
M. KaiJ. Lochard

Editorial members
C.H. Clement (Scientific Secretary & Annals of the ICRP Editor-in-Chief)
H. Fujita (Assistant Scientific Secretary & Annals of the ICRP Associate Editor) (2018– )H. Ogino (Assistant Scientific Secretary & Annals of the ICRP Associate Editor) (2016–2018)
Committee 1 members during preparation of this publication(2016–2017)
W. Rühm (Chair)
S. Bouffler (Vice-Chair)
D. Laurier (Secretary)
T.V. Azizova
N. BanR. Chakraborty
W. Dörr
M. Hauptmann
P. Rajaraman
D. Stram

Q. SunM. Tirmarche
R. Wakeford
A. Wojcik (2015–)(2017–2021)
W. Rühm (Chair)
A. Wojcik (Vice-Chair)
J. Garnier-Laplace (Secretary)
T.V. Azizova

M. Hauptmann
K. Ozasa
P. Rajaraman
K. SakaiD. Stram
Q. SunR. Wakeford
G. Woloschak

67

DRAFT REPORT FOR CONSULTATION: DO NOT REFERENCE
1856
1857
1858
1859
1860
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870
1871
1872
1873
1874
1875
1876
1877
1878
1879

R. Chakraborty (–2018)
W. Dörr (–2019)

S. Salomaa
M. Sokolnikov

Main Commission members at the time of approval of this publication
Chair: C. Cousins, UK
Vice-Chair: J. Lochard, France
Scientific Secretary: C.H. Clement, Canada; sci.sec@icrp.org *K.E. Applegate, USA
S. Bouffler, UK
K.W. Cho, Korea
D.A. Cool, USA
J.D. Harrison, UK
M. Kai, Japan
C.-M. Larsson, Australia
D. Laurier, France

S. Liu, China
S. Romanov, Russia
W. Rühm, Germany

Emeritus Members
R.H. Clarke, UK
F.A. Mettler Jr., USA
R.J. Pentreath, UK
R.J. Preston, USA
C. Streffer, Germany
E. Vaño, Spain* Although formally not a member since 1988, the Scientific Secretary is an integral part of the Main
Commission
ICRP and the members of Task Group 102 thank R. Wakeford for his valuable comments to thispublication.

68